# Machine Learning and Deep Learning Methods for Building Intelligent Systems
in Medicine and Drug Discovery: A Comprehensive Survey

Jignesh Chowdary G Suganya G<EMAIL_ADDRESS>Premalatha M Asnath Victy
Phamila Y Karunamurthy K Vellore Institute of Technology Chennai, Tamilnadu,
India

###### Abstract

With the advancements in computer technology, there is a rapid development of
intelligent systems to understand the complex relationships in data to make
predictions and classifications. Artificail Intelligence based framework is
rapidly revolutionizing the healthcare industry. These intelligent systems are
built with machine learning and deep learning based robust models for early
diagnosis of diseases and demonstrates a promising supplementary diagnostic
method for frontline clinical doctors and surgeons. Machine Learning and Deep
Learning based systems can streamline and simplify the steps involved in
diagnosis of diseases from clinical and image-based data, thus providing
significant clinician support and workflow optimization. They mimic human
cognition and are even capable of diagnosing diseases that cannot be diagnosed
with human intelligence. This paper focuses on the survey of machine learning
and deep learning applications in across 16 medical specialties, namely Dental
medicine, Haematology, Surgery, Cardiology, Pulmonology, Orthopedics,
Radiology, Oncology, General medicine, Psychiatry, Endocrinology, Neurology,
Dermatology, Hepatology, Nephrology, Ophthalmology, and Drug discovery. In
this paper along with the survey, we discuss the advancements of medical
practices with these systems and also the impact of these systems on medical
professionals.

###### keywords:

Machine learning, Deep learning, Medical specialities, Optimization, Complex
diseases, Neural networks, CNN for healthcare, Healthcare Datasets.

††journal: Medical Image Analysis

##  1 Background

Machine learning and deep learning are the subsets of artificial intelligence
as shown in figure 1, that have witnessed rapid growth over the years. These
techniques have proved to be effective in diagnosing diseases across various
specialties in medicine.

Fig. 1:  AI, ML and DL

###  1.1 Machine learning

Machine Learning techniques are statistical models that are used to make
predictions and classifications on the given data. The machine learning models
are categorized into two types based on the type of learning techniques namely
supervised and unsupervised [6]. In supervised learning [105], the machine
learning model is trained with a set of input data (or features) that are
associated with known output. Once the training of the machine learning model
is successful, it is capable of making predictions on the new data. The
predictions made by the supervised-learning algorithms can be continuous or
discrete. Some of the examples of supervised learning algorithms are random
forests, decision trees, logistic regression, K-nearest neighbors, and support
vector machines [67, 113, 76, 71]. These techniques are used for diagnosing
diseases like diabetes, thyroid, diabetic retinopathy, cardiomyopathy, etc. On
the other hand, unsupervised techniques operate on input data without any
labels(or outcomes). The main goal of these algorithms is to identify the
undefined patterns in the unlabeled dataset. These techniques are mainly used
for reducing the dimensions of the dataset by extracting important features
from the data. By reducing the number of features, issues like high
computational cost and multicollinearity can be avoided [26]. Some of the most
commonly used unsupervised learning algorithms are latent Dirichlet analysis
and principal component analysis.

###  1.2 Deep learning

Deep learning is a subset of the broad family of machine learning. Deep
learning models are successful in solving problems related to image
classification and natural language processing. These deep learning algorithms
[53] are based on neural networks, these networks have multiple layers for
extracting high-level features and for eliminating problematic features, so
the performance of deep learning algorithms is higher than machine learning
algorithms. Some of the widely used deep learning architectures [104] are
Convolutional Neural Networks(CNN), Recurrent neural networks(RNN), and
generative adversarial neural networks(GAN). Convolutional neural networks
[4][ are mostly used for solving computer vision and image classification
tasks. CNN’s reduce the number of parameters compared to fully connected
neural networks. Convolutional neural networks are used for organ
segmentation, identification of tumors, classification of various cancers from
radiology images. The basic structure of CNN for classification and
segmentation are shown in Figure 2.

Fig. 2: Basic CNN architectures for classification and segmentation.

RNN’s are used for solving audio recognition and natural language processing
problems. RNN’s [45] can be used for sequential data analysis as they show
temporal dynamic behavior. This property of RNN can be used for aiding
fractional radiotherapy. GAN’s are generative models [130], when given a
training dataset these models are capable of generating new data that have the
same statistics as the training data. GAN’s learn with an adversarial
competition between its discriminator and generator. GAN’s are used mainly for
medical image synthesis and for image augmentation of the training data to
avoid problems like overfitting and data scarcity. The basic structures of RNN
and GAN are shown in figure 3.

Fig. 3: Basic structure for Recurrent neural network and Generative adhersal
network.

In this paper, we present a survey of various machine learning(ML) and deep
learning(DL) models that can be used for building intelligent systems for the
diagnosis of various diseases in 16 medical specialties of healthcare. Along
with the 16 medical specialties we present the survey of ML and DL models in
drug discovery. The 16 medical specialties that are considered for this work
are Dental medicine, Haematology, Surgery, Cardiology, Pulmonology,
Orthopedics, Radiology, Oncology, General medicine, Psychiatry, Endocrinology,
Neurology, Dermatology, Hepatology, Nephrology, and Ophthalmology. Along with
the survey, we discuss the advancements in medicine with machine learning and
deep learning-based diagnostic systems and also the impact of these systems on
medical professionals. The rest of the paper is organised as follows, Section
2 describes the survey methodology, Section 3 describes the literature review,
Section 4 discusses the impact of these methods, Section 5 concludes the work
and finally Section 6 presents the suggestions for future work.

##  2 Survey methodology

The articles that were surveyed in this paper are presented and published in
high-quality conferences and journals of IEEE, Elsevier, ACM, and Springer.
The terms that are used for searching these articles include machine learning,
deep learning, dental medicine, hematology, surgery, cardiology, pulmonology,
orthopedics, radiology, oncology, general medicine, psychiatry, endocrinology,
neurology, dermatology, hepatology, nephrology, ophthalmology, and drug
discovery. The articles that are considered for this survey are directly
related to the topic of machine learning and deep learning applications in
medicine and drug discovery. For this work, both empirical and review articles
related to the above topics were considered.

##  3 Literature review

###  3.1 Dental medicine

Table 1 shows some of the popular approaches used by the researchers to
diagnose dental diseases. [87] used machine learning approaches to classify
three different diseases, namely dental caries, periodical infection, and
periodontitis. The authors considered a dataset of 251 radio videography (RVG)
x-ray images for classification, out of which 80 images are of dental caries,
110 images are of periodical infection, and 61 images of periodontitis. Three
models were built they are Convolutional neural network(CNN), transfer
learning of CNN with VGG16 as a feature extractor, and CNN transfer learning
model with fine-tuning. A fuzzy rule-based model is designed by [116] to
diagnose dental diseases. In this model, different methods are used for
feature extraction; they are entropy, edge-value, intensity, and local binary
patterns, gradient feature, red-green-blue, and patch level feature. These
extracted features are used to make a database. For this work, Fuzzy c-means
clustering and Mamdani’s fuzzy interface methods were used. Machine learning
algorithms are used by [119] for optimizing the dental milling process. In
this paper, two models of feedforward networks were designed with Bayesian
regularisation and early stopping technique. The artificial neural network
structure of a feedforward network built using Bayesian regularisation has two
layers, the first layer with 5 hyperbolic target units and the second layer
with 30 hidden hyperbolic target units and one output unit. The structure of
the second feedforward network built using the early stopping technique has
the same layers as the first model, but these two networks are estimated
separately using the Lenvenberg-Marquardt method and quasi-Newton methods. The
performance of these models is shown Table 1.

Table 1: ML and DL methods in Dental medicine.

Author | Method | Diagnosis/Application | Metrics | Findings
---|---|---|---|---
[87] | CNN |  Dental caries, periodical infection, and periodontitis | Accuracy | 0.7307
Transfer learning | 0.8846
| Transfer learning with fine
---
tuning
0.8846
[116] | Fuzzy Interface System |  Abnormalities in dental x-ray | Mean Square Error | 0.2445
Mean Absolute Error | 0.1264
Accuracy | 90.29%
[119] |  | Feedforward network
---
with early stopping
Optimization of dental mining process | Normalized Mean error | 89.95%
| Feedforward network
---
with Bayesian regularisation
Normalized Mean error | 87.98%

###  3.2 Haematology

The diagnosis of blood-related diseases is very complicated and time-
consuming. Machine learning techniques are used by Haematologists to diagnose
diseases accurately in less time. [78] used machine learning classification
techniques to build a model that automates the differential blood counting
system. The segmentation of images is done using contour models, namely snakes
and balloons. Texture and shape-based features are considered for
classification. Four machine learning classifiers are used for classification;
they are K-Nearest Neighbour, Linear vector quantization, Multilayer
perceptron, and support vector machine. The accuracies of the classifiers are
shown in table LABEL:tb2. The application of machine learning for the
segmentation and tracking of thrombus is proposed by [85]. Adaboost and
Decision tree models are used for segmentation, and the K-nearest neighbor
algorithm is used for tracking. These classification algorithms are evaluated
based on the Dice coefficient, and positive predictive value and tracking
method are evaluated based on the Dice coefficient. The classification of
sickle cell disease using machine learning is proposed by [46]. For this work,
the performances of various machine learning algorithms are analyzed, as
mentioned in table 2, from which Levenberg-Marquardt and random forest
algorithms produced excellent results.

Table 2: ML and DL methods in Haematology.

Author | Method | Diagnosis/Application | Metrics | Findings
---|---|---|---|---
[78] | K-Nearest Neighbour |  Automation of differential blood counting system | Accuracy | 81%
Linear vector Quantization | Accuracy | 83%
Multi Layer Perceptron | Accuracy | 90%
Support Vector Machine | Accuracy | 91%
[85] | Decision Tree | Thrombus | Dice coefficient | 0.89 ±0.02
Positive predicted value | 0.94 ±0.04
Ada Boost | Dice Coefficient | 0.87 ±0.02
Positive predicted value | 0.90 ±0.05
Tracking | Positive predicted value | 0.94 ±0.05
[46] | Random Oracle model | Sickle cell disease | Accuracy | 53.9%
|  | Area Under ROC Curve | 0.524
|  | Levenberg-Marquardt
---
learning algorithm
| Accuracy | 99.2%
|  |  | Area Under ROC Curve | 0.991
|  | Trainable decision tree
---
Classifier
| Accuracy | 92.1%
|  |  | Area Under ROC Curve | 0.95
|  | Random Forest, Decision
---
Tree Ensemble Classifier
| Accuracy | 91.5%
|  |  | Area Under ROC Curve | 0.961
|  | Levenberg- Marquardt
---
learning algorithm and
Random Forest, combined
using Levenberg neural
network
| Accuracy | 99.2%
|  |  | Area Under ROC Curve | 0.988
|  | Levenberg- Marquardt
---
learning neural network
and Random Forest,
combined using Fischer
discriminate analysis
| Accuracy | 98.9%
|  |  | Area Under ROC Curve | 0.995
| Functional link neural network |  | Accuracy | 96.2%
|  |  | Area Under ROC Curve | 0.972
| Linear Combiner Network |  | Accuracy | 84%
|  |  | Area Under ROC Curve | 0.849

###  3.3 Surgery

Machine learning techniques are used as a decision support system for post-
operative planning and as a navigation system for many of the critical
surgeries. For understanding the functioning of knee implants after the
Surgery, [49] used machine learning techniques for building a statistical
model. The mapping function of the proposed model in this paper is built using
the pre and post-operative features, which are extracted using Principle
component analysis from the clinical database. The evaluation results are
shown in Table 3. Minimal invasive surgeries are being performed by surgeons
to avoid complications during the surgery and to reduce patient’s recovery
time. Most of the minimally invasive surgeries are operated using robots. For
avoiding the tremors during the procedure while operating with robots, [60]
presented a method for attenuating the tremors using machine learning. The
proposed method is based on optimizing parameters using particle swarm
optimization technique and building a hybrid support vector machine with an
integrated kernel to attenuate tremors. Stapedotomy is a surgical procedure
performed to improve hearing, which requires the estimation of the thickness
of the stapes bone, which is usually done by drilling a hole through the bone.
[44] used machine learning techniques for the estimation of thickness. For the
estimation, the author used a machine learning scheme known as Fuzzy Lattice
Neurocomputing. In complex trauma surgeries, the surgeons are using augmented
reality to understand the relationship between anatomy, tools, and implants.
Olivier [84] proposed a model to improve surgical scene understanding using
augmented reality. For this work, the random forest was used to identify
objects in the scene, and the pixel-wise alpha map was created using an
object-specific lookup table for image fusion. The data for this model is
obtained from the C- arm devices with the Kinect RGB-Depth sensor. [70] used
machine learning algorithms like K-nearest neighbor, naive Bayes, and
perceptron algorithm with uneven margins to classify semi-structured and
unstructured pathology reports. He highlighted that the K-nearest neighbor
algorithm built with binary occurrence type word vector, stop word filter, and
pruning produced high accuracy in classifying semi-structured reports.

Table 3: ML and DL methods in Surgery.

Author | Methods | Diagnosis/Application | Metrics | Findings
---|---|---|---|---
[49] |  | Statistical Kinematics
---
model
| Understanding the function
---
of knee implants after the
surgery
| mean correlation
---
coefficients
0.79
|  |  |  | mean
---
root-mean-squared-error
3.44 mm
[70] |  | K-nearest neighbour
---
algorithm with binary
occurrence type word
vector, stop word
filter and pruning
| Classification of unstructured
---
pathology reprots
Accuracy | 99.4%

###  3.4 Cardiology

Cardiovascular diseases are life-threatening diseases that require a high
degree of accuracy and precision for their diagnosis. Machine learning has
increased the rate of accuracy in diagnosing diseases. [40] described the use
of machine learning to monitor patients having cardiovascular diseases. The
application of Deep neural networks to detect myocardial infarction(heart
attack) from Electrocardiogram(ECG) signals was proposed by [1]. The neural
network used for this work consists of 11 convolution layers out of which six
layers are activated by Rectified Linear Unit(ReLU) activation function, and
the last layer is activated with soft-max function, the pooling function used
is Max-pooling. The evaluation of results is done for both noisy and noiseless
beat signals in the dataset. A novel approach was proposed by Peshala [110] to
detect heart failure with preserved ejection fraction by using a statistical
model along with a supervised algorithm. The data for this work is
preprocessed by using the KNNimpute approach, and three principal component
analysis models were built for the spatiotemporal representation of rest and
exercise, and distance weighted nearest neighbor algorithm is used for the
diagnosis. A comparative analysis was performed by [5] in using machine
learning network structures to diagnose diabetes and cardiovascular diseases.
According to this analysis, the network built using the Levenberg-Marquardt
learning algorithm(LMLA) and Naive Bayesian network achieved better accuracy
compared with other proposed models considered from 2008 to 2017. Hypertrophic
cardiomyopathy(HCM) is a chronic heart disease that leads to the thickening of
the heart muscle and restricts the flow of blood. [91] proposed a methodology
to detect hypertrophic cardiomyopathy from 10 seconds, 12-lead
electrocardiogram(ECG) signals. For this work, features are selected using
information gain criterion, and random forest, and support vector machines are
used for diagnosis. The application of machine learning techniques to diagnose
heart disease in patients is proposed by [25]. The data for this work is
preprocessed to make it suitable for classification. A set of five machine
learning algorithms like logistic regression, random forest, gradient
boosting, support vector machine, and naive Bayes are used to make
classification’s on the data. The accuracies of these classifiers are
mentioned in table 4, out of which logistic regression has maximum accuracy.
An ensemble learning methodology was proposed by [22] to diagnose heart
diseases. This methodology is based on the ensemble voting of Logistic
Regression, artificial neural network, Gaussian naïve Bayes, random forest,
and K-nearest neighbor algorithms. Their model achieved an accuracy of 89$\%$,
precision of 91.6$\%$, sensitivity of 86$\%$, and specificity of 91$\%$.
Atherosclerosis is the primary factor for cardiovascular diseases like a heart
attack. A novel methodology was proposed by [127], which focuses on the
detection of risk factors from the different stages of carotid atherosclerosis
that are leading to cardiovascular diseases. This method focuses on the use of
a decision tree algorithm with a neural network, logistic regression, and
support vector machine for making accurate predictions. Seismocardiographic
signals are used to study cardiopulmonary interactions. [31] used k-means
clustering along with a time-domain amplitude feature to cluster
Seismocardiographic signals. Single-photon emission computed tomography(SPET)
imaging is a nuclear imaging technique used to capture the functioning of the
heart using gamma rays. [14] used machine learning to combine clinical
information and SPET imaging data to predict major adverse cardiac
events(MACE). The use of machine learning for rehabilitation assessment is
proposed by [88]. Thermal and heart rate data is processed, and the Neural
network with sigmoidal and probabilistic transfer function is used for the
assessment.

Table 4: ML and DL methods in Cardiology.

Author | Method | Diagnosis/Application | Metric | Findings
---|---|---|---|---
[1] | Convolutional Neural Network | Myocardial infarction | Accuracy |  | noisy beats: 93.53
---
noiseless beats: 95.22
Sensitivity |  | noisy beats: 93.71
---
noiseless beats: 95.49
Specificity |  | noisy beats: 92.83
---
noiseless beats: 94.19
[110] |  Statistical model and Distance- weighted -nearest-neighbor (DWNN) | Heart failure | Sensitivity | 86%
Specificity | 82%
Area under ROC Curve | 0.89
Accuracy | 85%
[5] |  Multilayer feedforward neural network with Levenberg-Marquardt learning algorithm. |  Diabetes and cardiovascular disease in general | Accuracy | 87.29%(diabetes)
| 89.38%
---
(Cardio Vascular Disease)
Naïve Bayesian network | 99.51%(diabetes)
| 97.52%
---
(Cardio Vascular Disease)
[91] |  For Random Forest and Support Vector Machine | Hypertrophic cardiomyopathy | Precision | 0.84
Recall | 0.89
Specificity | 0.93
F-Measure | 0.86
[25] | Logistic Regression | Heart disease in general | Accuracy | 0.8651685
Random Forest | 0.8089888
Naive Bayes | 0.8426966
Gradient Boosting | 0.8426875
Support Vector Machine | 0.7977528
[22] |  Ensemble Voting of Logistic regression, artificial neural network, gaussian naive bayes, random forest and K-nearest neighbor. | Heart disease in general | Accuracy | 89%
Precision | 91.6%
Sensitivity | 86%
Specificity | 91%
[127] |  | Decision Tree algorithm with support
---
vector machine,
logical regression and neural networks
Carotid atherosclerosis | Accuracy | 87.2%

###  3.5 Pulmonology

Respiratory diseases are very complex diseases that affect not only the lungs
but also the other organs of the body. Predicting these diseases in advance
can reduce the complications while treating these diseases. Many studies are
performed by researchers in diagnosing respiratory diseases by using machine
learning techniques. Acute diseases like Respiratory distress syndrome(ARDS)
is diagnosed using machine learning by [92]. For this work, four different
versions of the Support vector machine were built. The performance of these
algorithms is evaluated based on accuracy and area under the receiver
operating characteristic(ROC) curve shown in table 5. Audio auscultation is
the primary examination that a specialist performs when he sees a patient.
Audio auscultation is considered to be the best primary practice to detect
abnormalities in the heart and lungs. An approach was performed by [18] to
detect respiratory diseases using audio auscultation. In this approach, the
automatic free sound extractor was used to extract features from the
ICBHI-2017 challenge database, and a boosted decision tree algorithm is used
to detect sounds with respiratory diseases. A study was performed by [80] in
diagnosing respiratory pathologies from pulmonary acoustic signals using
support vector machine and K-nearest neighbor algorithms. A comparative study
was performed by [93] to detect obstructive sleep apnoea by using
electrocardiogram derived respiratory signal(EDR) and respiratory chest
signals. Three models, namely Support Vector machine, linear discriminant
analysis, and extreme learning machine was built. These three models diagnosed
the disease with high accuracy when they are trained with respiratory chest
signals. Seismocardiogram cycles were used to identify respiratory phases like
inspiration and expiration using machine learning by [131]. For building, this
model support vector machine was employed and trained with the 50 percent data
approved by healthcare authorities of British Columbia. A novel methodology
named FissureNet was proposed by [33] to detect pulmonary fissure from
computed tomography(CT) images. This FissureNet is a source of a fine cascade
of two Seg3DNet neural network algorithms. The proposed methodology achieved
higher accuracy when compared to models built with U-Net architecture and
Hessian matrix.

Table 5: ML and DL methods in Pulmonology.

Author | Method | Disease | Metrics | Findings
---|---|---|---|---
[92] |  Support Vector Machine with a class weighted cost function Sampled Randomly | Respiratory distress syndrome | Accuracy | 0.7478
| Area under
---
ROC Curve
0.7703
Support Vector Machine with a class weighted cost function without any sampling | Accuracy | 0.7094
| Area under
---
ROC Curve
0.7122
Support Vector Machine with Label Uncertainty sampled randomly | Accuracy | 0.7698
| Area under
---
ROC Curve
0.7989
Support Vector Machine with Label Uncertainity without any sampling | Accuracy | 0.7188
| Area under
---
ROC Curve
0.7431
[18] | Boosted Decision Tree |  | Respiratory abnormalities
---
from audio auscultation
Accuracy | 85%
[131] | Support Vector Machine |  | Respiratory phase identification
---
(inspiration and expiration)
Accuracy | 88.4%
[33] |  | FissureNet
---
(Coarse to fine cascade of two Seg3DNets)
Pulmonary fissure |  | Area under
---
the precision
recall curve
0.991

###  3.6 Orthopaedics

The risk of having diseases like osteoporosis, arthritis is predicted using
machine learning. A methodology was proposed by [48] for the risk assessment
of osteoporosis. In this methodology, the Support Vector machine is used for
risk assessment, and this method was tested on the Korean government’s
Survey’s dataset. The diagnosis of rheumatoid arthritis from X-ray images was
proposed by [66]. Support Vector Machine, along with a histogram of oriented
gradients, is used for the diagnosis. The accuracy of the model detecting
finger joints, erosion estimation, and Joint space narrowing(JSN) is shown in
table 6. The assessment of bone density from x-ray images was proposed by
[15]. In the proposed methodology, the traditional GoogleNet convolutional
neural network was modified by adding large number filters with a multilayer
perceptron. This modified neural network produced better results than the
traditional neural network.

Table 6: ML and DL methods in Orthopaedics.

Author | Method | Diagnosis/Application | Metric | Findings
---|---|---|---|---
[48] | Support Vector Machine | Risk assessment of osteoporosis | Area under ROC Curve | 0.827
Accuracy | 76.7%
Sensitivity | 77.8%
Specificity | 76.0%
[66] | Support Vector Machine | Rheumatoid arthritis | Accuracy |  | 81.4%
---
(detection of finger joints)
| 50.09
---
(erosion estimation)
| 64.3%
---
(JSN score)
[15] |  | Modified GoogleNet
---
convolution neural network
Bone density assessment | Accuracy | 91.3%

###  3.7 Radiology

Radiology deals with accurate, precise diagnosis and treatment of many
diseases. Machine learning algorithms were used by [55] for gating and
tracking of lung cancer tumors. In this work, Principle component analysis and
neural network with backpropagation algorithm are used for gating, and an
artificial neural network is used for tracking. The results are shown in Table
7. [112] compared the performance of three convolutional neural network
architectures namely GoogleNet, InceptionV3, and ResNet50 to detect
abnormalities in a chest x-ray. For this work, the images from the Stanford
Normal Radiology Diagnostic Dataset are used and are preprocessed using
histogram equalization before training the CNN models. Detecting fractured
bones from x-ray images was proposed by [17]. For this work, many types of
features were fused for building a stacked random forest model to detect the
fractures of bones. This stacked random forest showed enhanced performance
compared to the support vector machine. Machine learning classifiers and
active learning methods are used by [74], to classify radiology reports. In
this work, four active learning methods like Self-Confident, Simple, Balance
EE, and Kernel Farthest-First are analyzed to enhance the performance of the
Support vector machine classifier for the classification of reports. The rule-
based preprocessing method is used to preprocess the data. A study was
performed by [123] on using machine learning for the classification of
Malignant and Benign microcalcifications. In this work, five different machine
learning and deep learning algorithms, namely Support Vector Machine, Kernel
Fisher Discriminant, Relevance Vector Machines, Feed Forward Neural Network,
and Ada Boost, were studied and their performances were evaluated. The
performances of the classifiers are described in table 7. [126] used machine-
learning to detect breast cancer from mammogram images. For this approach, a
three-layered feedforward neural network with a backpropagation algorithm was
built to detect breast cancer. This neural network is trained on features
extracted from mammograms by experienced radiologists. Abnormalities from
orthopedic trauma radiographs are diagnosed using neural network approaches by
Jakub [77]. Five freely available networks from the Caffe library are
considered, and they are retrained with 13 epochs. The performance of all the
networks are similar and exhibited an accuracy of 90

Table 7: ML and DL methods in Radiology.

Author | Method | Application/Diagnosis | Metrics | Findings
---|---|---|---|---
[55] |  | Principal Component Analysis and
---
Artificial Neural Network. (Gatting)
Gating and tracking of lung cancer tumours | Precision | 96.5
Artificial Neural Network(tracking) | mean localization error | 2.1 pixels
| maximum error at 95%
---
confidence level
4.6 pixels
[112] | CNN with GoogLeNet architecture | Abnormality detection of chest x-ray | Accuracy | 0.8
F1 Score | 0.66
cite{t61} | Stacked Random Forests Feature fusion. | Bone fractures | Precision | 24.7%
[74] | Support Vector Machine | Classification of radiology reports | Sensitivity | 98.25%
Specificity | 96.14%
[123] | Support Vector Machine | Malignant and Benign microcalcifications | Standard Deviation | 0.0259
Kernel Fisher Discriminant | Standard Deviation | 0.0254
Relevance Vector Machines | Standard Deviation | 0.0243
Feed Forward Neural Network | Standard Deviation | 0.0266
Ada Boost | Standard Deviation | 0.0268
[126] | Artificial Neural network | Breast cancer | Area under ROC Curve | 1

###  3.8 General medicine

Computer-aided diagnosis is used in general medicine to diagnose diseases
based on the symptoms of the patient. The Survey of machine learning models in
general medicine is shown in Table 9. A machine learning approach was proposed
by [t74] to improve the performance of computer-based diagnosis systems. For
this purpose, a semi-supervised learning algorithm named co-Forest was used.
[68] proposed a model to diagnose fever. This model was build using the Naive
Bayes algorithm with Dempster Shafer theory. Dengue Hemorrhagic Fever is a
fatal disease that was found mostly in the southern parts of Asia. For
estimating the risk level of this disease, a machine learning model was
proposed by [69]. The risk level of the disease is predicted using an extreme
learning machine. For avoiding aspiration pneumonia, a swallowing assessment
model was developed using machine learning techniques by [41]. The features
are extracted using linear predictive coding, and a support vector machine is
used to classify patients with dysphagia. Convolutional neural networks were
used by [54] to diagnose malaria. The performance of the neural network is
superior to the performance of the support vector machine.

Table 8: ML and DL methods in General Medicine. Author | Method | Application/Diagnosis | Metric | Findings
---|---|---|---|---
[68] |  | Dempster Shafer with Naive
---
Bayes
Fever | Accuracy | 56.25%
[69] | Extreme learning machine |  Dengue Hemorrhagic Fever | Mean Absolute Error | 0.08698
| Mean Absolute
---
Percentage Error
3.00536
[41] |  Support Vector Machine with Linear predictive coding | Dysphagia | Sensitivity | 82.4%
Specificity | 86%
[54] | Convolutional neural network | Malaria | Accuracy | 97.37%
Sensitivity | 96.99%
Specificity | 97.75%
F1-Score | 97.36%
Precision | 97.73%
| Matthews correlation
---
coefficient
94.75%

###  3.9 Oncology

Diseases like cancer need accurate prediction for their diagnosis and
treatment. A comparative study was performed by [102] on using machine
learning to diagnose cervical cancers. Various existing machine learning
models were analyzed in this study. [117] performed an analysis of using
machine learning for diagnosing cancer. Machine learning techniques like
Xgboost, Deepboost, BoostI, and support vector machine are studied. The
performances of these algorithms were shown in table 8. A model was proposed
by [52] to diagnose breast cancer using a genetic algorithm. This genetic
algorithm obtained an accuracy of 97.2The detection of colon cancer from CT
colonography images was proposed by [34]. A histogram of oriented gradients is
employed to extract features, and a fully connected neural network was used
for diagnosis. Convolutional neural networks performed better than other
existing algorithms for diagnosis. Many cancer-related deaths in men are due
to prostate cancer. A neural network model was developed by [115] to detect
prostate cancers from multi-parametric MRI scan images. This convolutional
neural network has 5 convolutional layers, and it is evaluated based on the
area under the ROC curve. Machine learning and deep learning techniques are
applied to classify cancerous profiles by [101] For this work, a feedforward
neural network with a backpropagation algorithm and support vector machine is
used for clustering and classification of data. Neural network approaches were
used by [10] to detect lung cancer from chest x-ray images. For this work, 121
layered neural network and transfer learning methods are employed for building
the model. The performance of the model over three different datasets are
shown in Table 8. The waiting times of patients in radiational oncology were
predicted using machine learning techniques by [42]. For this study, a set of
four different algorithms, namely, support vector machine, linear regression,
random forest, and decision tree, were analyzed. Random Forest algorithm
performed best when compared to the remaining three algorithms.

Table 9: Ml and DL methods in Oncology.

Author | Method | Diagnosis/Application | Metrics | Findings
---|---|---|---|---
[117] | Xgboost |  Thyroid cancer, colon cancer and liver cancer |  | Average mean
---
area under Curve
| Thyroid nodule: 0.811
---
Colon: 0.872
Liver: 0.797
AMACC |  | Thyroid nodule: 0.798
---
Colon: 0.857
Liver: 0.775
Support Vector Machine |  | Average mean
---
area under Curve
| Thyroid nodule: 0.750
---
Colon: 0.897
Liver: 0.897
AMACC |  | Thyroid nodule: 0.726
---
Colon: 0.890
Liver: 0.895
DeepBoost |  | Average mean
---
area under Curve
| Thyroid nodule: 0.758
---
Colon: 0.822
Liver: 0.791
AMACC |  | Thyroid nodule: 0.744
---
Colon: 0.833
Liver: 0.786
Boost I |  | Average mean
---
area under Curve
| Thyroid nodule: 0.500
---
Colon: 0.500
Liver: 0.500
AMACC |  | Thyroid nodule: 0.658
---
Colon: 0.694
Liver: 0.652
[52] | Genetic Algorithm | Breast cancer | Accuracy | 97.2%
[34] | Convolutional Neural Network | Colon cancer | Accuracy |  | Colon detection: 87.03%
---
Polyp Detection In colon: 88.56
Sensitivity | Polyp Detection In Colon: 88.77%
Specificity | Polyp Detection In Colon: 87.35%
cite{t70} | Deep Convolutional Neural Network | Prostate cancer | Area under ROC Curve | 0.903±0.009
[101] |  | Feed Forward Network and
---
(clustering)
Cancer profile classification | Accuracy | 87.51 (for 100 samples)
Support Vector Machine(classification) | Accuracy | 93.57 (for 100 samples)
[10] |  121-layer Densely Con- nected Convolutional Network | Lung cancer | Mean Accuracy | 74.43±6.01%
Mean Specificity | 74.96±9.85%
Mean Sensitivity | 74.68±15.33%
[42] | Random Forest Regressor |  Prediction of patients waiting time | Median Absolute Error [min] | 3.3
Mean Absolute Error [min] | 4.6
Standard Deviation Error [min] | 6.1
R-squared | 0.47

###  3.10 Psychiatry

Psychiatric disorders are common health problems with which millions of people
are suffering around the world. Machine learning and deep learning helps to
diagnose problems like depression based on data from many sources. The models
surveyed to diagnose psychiatric disorders are shown in Table 10. An approach
was proposed by [125] to classify people with depression based on the data
from social networking sites. A multilayer neural network was used for the
classification of users with depression from social network data. Another
methodology was proposed by [9] to classify depression using deep learning.
For this method, the particle swarm optimization algorithm is used for feature
extraction, and Meta Cognitive neural network with projection-based learning
is used for classification. A study was performed by [94] to diagnose
depression using machine learning in diabetes mellitus type 2 patients. For
this study, four machine learning algorithms, namely the K-means, Fuzzy
C-mean, probabilistic neural network, and support vector machine, were built.
The performance of the Support vector machine is better compared to the
remaining algorithms. For the detection of stress, a machine learning
framework was proposed by [109]. Stress at different levels was diagnosed from
Electrocardiogram signals using a logistic regression model. Psychiatric
disorders are found in newborn babies, which were inherited from their
depressed parents. A study was performed by [56] to detect familiar depression
in patients from MRI scans using machine learning and deep learning. In this
study, logistic regression, graph convolutional neural network, and Support
vector machine were analyzed. Various machine learning algorithms were
compared by [96] to diagnose depression and anxiety among patients. Five
different classifiers, namely Logistic Regression, Random Forest, CatBoost,
Naive Bayes, and Support Vector machine, were evaluated. A comprehensive
survey was performed by [82] on using machine learning to build systems that
detect mental stress. In this Survey, popular feature selection methods are
discussed. A comparative study was performed by [32] on using random forest
and neural networks to classify depression in patients based on motor
activity. The performance of random forest is better when compared to the
neural network. A hybrid model was developed by [39] to predict the emotional
state of learners from brain waves using machine learning techniques. WEKA
software is used for this approach, and the emotional states were predicted
using the K-nearest neighbor algorithm.

Table 10: ML and DL methods in Psychiatry.

Author | Algorithm | Application/Diagnosis | Metrics | Findings
---|---|---|---|---
[9] |  | Meta-Cognitive Neural
---
Network using
Projection-based learning
framework and particle
swarm optimization
| Classification of
---
depression
Efficiency | 85.94% (Average)
|  |  |  | 89.4% (Overall)
|  |  | Sensitivity | 74.11% (Average)
|  |  |  | 96.55% (Average)
[94] | Support Vector Machine | Depression | Accuracy | 96.475
| Fuzzy C_MEAN |  |  | 95.455
| Probabilistic Neural Network |  |  | 93.75
| K-Means |  |  | 87.879
[109] | Logistic Regression Classifier | Stress | Accuracy |  | 94.6% for
---
two level identification
|  |  |  |  | 83.4% for
---
multilevel identification
[56] |  | Logistic Regression with
---
regularisation
Familiar depression | Accuracy | 97.78%
| Support Vector Machine |  |  | 93.67%
|  | Graph convolution neural
---
network
|  | 89.58%
[96] | CatBoost | Depression and anxiety | Accuracy | 89.3%
| Logistic Regression |  |  | 87.5%
| Support Vector Machine |  |  | 82.1%
| Naive Bayes |  |  | 82.1%
| Random Forest |  |  | 78.6%
[82] | Random Forest | Stress | F1-Score | 73%
|  |  |  | Matthews correlation
---
coefficient
0.44
| Neural Network |  | F1-Score | 69%
|  |  |  | Matthews correlation
---
coefficient
0.35
[32] | K-Nearest Neighbour | Depression | Precision | 79.2% - 84%
|  |  | Recall | 78.7% - 82.2%
|  |  | F-Measure | 79.2% - 83.5%
|  |  | KAPPA | 0.78
|  |  | Sensitivity | 82.27%

###  3.11 Endocrinology

####  3.11.1 Diabetes

Diabetes is a major cause of many deadly diseases like cardiovascular
diseases, brain strokes, and many more. The observations from surveyed papers
are recorded in table 11. An approach was proposed by [27] to diagnose
diabetes using machine learning. For this approach, a random forest classifier
was used, and its performance was superior compared with logistic regression
and support vector machine. A study was performed by [108] to diagnose
diabetes mellitus using a support vector machine. For this study, linear and
non-linear support vector machines were analyzed and tested on a standard
dataset from the UCI repository. A study was performed by [3] on using machine
learning for preemptive diagnosis of diabetes mellitus. In this study, four
machine learning algorithms, namely naive Bayes, k-nearest neighbor, support
vector machine, and neural network, were analyzed. Artificial neural networks
outperformed all the remaining three classifiers. Using a Support vector
machine with radial bias function(RBF) kernel for diagnosing diabetes mellitus
was proposed by [12]. For this work, the data is sampled using a K-means
clustering algorithm. An approach was proposed by [111] to predict the rate of
re-admission of diabetes mellitus patients at the hospital. C.45 algorithm is
used for prediction on a standard dataset from the UCI machine learning
repository.

Table 11: ML and DL methods in Endocrinology(Diabetes).

Author | Method | Diagnosis/Application | Metric | Findings
---|---|---|---|---
[27] | Random Forest | Diabetes | Accuracy | 84%
[108] | Support Vector Machine | Diabetes mellitus | Accuracy | 80.20%
[3] | Artificial Neural Network | Diabetes mellitus | Accuracy | 77.50%
| Support Vector Machine |  |  | 71.25
| K-Nearest Neighbor |  |  | 73.75%
| Naive Bayes |  |  | 67.5%
[12] | Support Vector Machine | Diabetes mellitus | Accuracy | 94%
|  |  | Sensitivity | 93%
|  |  | Specificity | 94%
[111] | C4.5 Algorithm | Prediction readmission rate of diabetes patients at hospitals | Accuracy | 74.5%

####  3.11.2 Thyroid

A comparative study was performed by [64] to diagnose thyroid disease. In his
study, he analyzed six models, namely perceptron network, a neural network
with Radial based function(RBF) kernel, artificial immune recognition system,
Learning Vector Quantization, and feedforward network with backpropagation
algorithm. The performance of these models is shown in table 12. Classifying
the type of thyroid using machine learning was proposed by [2]. A support
vector machine classifier is used to make classifications on the real-time
data obtained from the UCI repository and a well-known hospital in Pakistan.
The segmentation and classification of thyroid images from ultrasound scans
were proposed by [98]. For this model, the Extreme learning machine and
support vector machine is used for segmentation and classification of
ultrasound images. The performance of these models is shown in Table 12.

Table 12: ML and DL methods in Endocrinology(Thyroid).

Author | Method | Diagnosis/Application | Metrics | Findings
---|---|---|---|---
[64] | A Neural network with RBF | Thyroid | Accuracy | 95.35%
| Perceptron |  |  | 91.16%
|  | Artificial Immune Recognition
---
system
|  | 93.5%
| Back Propagation algorithm |  |  | 69.77%
| Multi-layer Perceptron |  |  | 96.74%
| Learning Vector Quantization |  |  | 93.50%
[2] | Support Vector Machine | Thyroid | Accuracy | 95.7%
[98] | Support Vector Machine | Thyroid | Accuracy | 84.78
| Extreme Learning Machine |  | Accuracy | 93.56

###  3.12 Neurology

Machine learning and deep learning methods are used in neurology to diagnose
neurological disorders at an early stage. The performance of the models used
to diagnose neurological disorders is shown in Table 13. The diagnosis of
chronic diseases like schizophrenia from MRI scans was proposed by [75]. For
this approach, the Boruta algorithm is used for feature selection, and various
machine learning algorithms were built, namely Support vector machine, Linear
discriminant analysis, Naive Bayes, K-nearest neighbor, C5.0 Decision tree,
Gaussian process classifier, and random forest. A method was proposed by [132]
to classify normal and abnormal brain Computed Tomography(CT) scan images. The
features for this approach were extracted based on greyscale, shape and
texture, symmetric features. See5 and Neural network with radial bias function
was built based on these features. Cerebral microbleeds are chronic brain
hemorrhages that lead to the death of a person. The diagnosis of cerebral
microbleeds from Susceptibility weighted imaging(SWI) scans using deep
learning was proposed by [59]. Eight layered Convolutional neural network
model was built and trained with 8000 images for diagnosis. An approach was
proposed by [23] to recognize and anticipate the seriousness of tremor and
dyskinesia using machine learning and deep learning methods. For this work
three models namely the hidden Markov model, support vector machines, and
neural networks, were built for the detection of tremors and dyskinesia. The
seriousness of tremor and dyskinesia was anticipated using the Bayesian
maximum likelihood classifier. A diagnostic approach for dementia and mild
cognitive impairment using machine learning was proposed by Daniel [107]. For
this approach three models namely extreme gradient boosting, stochastic
gradient boosting, and the random forest was built using the features
extracted by the RelieF method. A two-step approach for classifying
Parkinson’s disease gait was proposed by [121]. In this approach, the first
step is to normalize the data using a multi regression approach, and the
second step is classification using random forest. The performance of this
approach is superior to the performance of the support vector machine and
kernel Fisher discriminant. The detection of epileptic convulsions from
accelerometric signals using machine learning was proposed by [65]. For this
work, the classification of seizure and non-seizure epochs is done using the
least-square support vector machine. The data for this work is collected from
the patients using four accelerometers tied to their wrists and ankles. [29]
proposed a neural network model for the assessment of Parkinson’s disease. For
this work convolutional neural network with a rectified linear unit(ReLU)
activation function was built, and it showed better performance compared to
K-nearest neighbor, support vector machine, AdaBoost, and partial decision
tree. Bradykinesia is an important factor in the diagnosis of Parkinson’s
disease. A method was proposed by [63] to predict the scores of each patient
based on their moments using a support vector machine. Movements like finger
tapping, diadochokinesis, and toe-tapping were considered. Classifying
neurological gait disorders like Parkinson’s disease and stroke using multi-
task feature learning was proposed by [83]. For building this multi-task
feature learning method, a support vector machine and neural network were
used. The segmentation of glioblastomas from brain MRI scans was proposed by
[43]. The proposed method focuses on replacing manual segmentation of brain
MRI scans with automated segmentation using machine learning. In this
approach, segmentation is done using K-means clustering, and the segmentation
accuracy of this model is better than the accuracy of manual segmentation. The
detection of chronic traumatic Encephalopathy using machine learning was
proposed by [58]. For this work, Random classifier, Random Forest, Support
vector machine(RBF kernel), and K-nearest neighbor, were built. These models
were trained with data from two sequences, namely PRESS and L-COSY of magnetic
resonance spectroscopy. The accuracy of the classifiers is better when they
are trained with L-COSY.

Table 13: ML and DL methods in Neurology.

Author | Method | Diagnosis/Application | Metrics | Findings
---|---|---|---|---
[75] | Support vector machine | Schizophrenia | Accuracy | 94.12%
| C4.5 Decision tree |  |  | 91.18%
| Random Forest |  |  | 91.18%
| K-Nearest Neighbours |  |  | 79.41%
| Linear discriminant analysis |  |  | 79.41%
|  | Gaussian process classifier
---
(laplacedot)
|  | 92%
| Naive Bayes |  |  | 85.29%
[132] | See5 | Brain abnormalities | Accuracy | 90%-94%
|  | Radial Bayes Function Neural
---
Network
|  | 77%-82%
[59] | Convolutional neural network | Cerebral microbleeds | Sensitivity | 97.29%
[23] | Neural Network |  | Detection of tremor
---
and dyskinesia
Global Error Rate |  | Tremor: 6.2%
---
Dyskinesia: 8.8%
| Support Vector Machine |  |  |  | Tremor: 7.2%
---
Dyskinesia: 9.1%
| Hidden Markov models |  |  |  | Tremor: 6.1%
---
Dyskinesia: 12.3%
|  | Bayesian maximum likelihood
---
classifier
| Seriousness of tremor
---
and dyskinesia
Sensitivity |  | Tremor: 96.3%
---
Dyskinesia: 99.3%
[107] |  | Extreme Gradient
---
Boosting
Dementia and mild cognitive impairment | Accuracy | 0.88
| Stochastic
---
Gradient
Boosting
0.87
Random Forest | 0.87
[121] | kernel Fisher discriminant | Parkinson’s disease | Accuracy | 86.2%
| Support Vector Machine |  |  | 80.4%
| Random Forest |  |  | 92.6%
[65] |  | Least-squares support vector ma-
---
chine classifier
Epileptic convulsions | Median sensitivity |  | 100%
---
(For seizures
longer than
30 seconds)
|  |  |  | False detection
---
rate
| 0.39 h-1
---
(For seizures
longer than
30 seconds
[29] | Convolutional Neural Network | Parkinson’s disease | Accuracy | 90.9
[63] | Support Vector Machine | Parkinson’s disease | Classification Errors |  | 15-16.5%
---
(finger tapping)
|  |  |  |  | 9.3-9.8%
---
(diadochokinesis)
|  |  |  |  | 18.2-20.2%
---
(toe tapping)
[83] | Multi Tasking Feature Learning | Parkinson’s disease |  | Area under
---
ROC Curve
0.96
[43] | K-Means Clustering | Glioblastomas | Dice Coefficiet | 0.82321
[58] | Random Classifier(L-COSY) |  | Chronic traumatic
---
Encephalopathy
Accuracy | 61%
|  | Support Vector
---
Machine-RBF(L-COSY)
|  | 38%
| Random Forest(L-COSY) |  |  | 61%
| K-Nearest Neighbours(L-COSY) |  |  | 87%
| Random Classifier(PRESS) |  |  | 45%
|  | Support Vector
---
Machine-RBF(PRESS)
|  | 60%
| Random Forest(PRESS) |  |  | 70%
| K-Nearest Neighbours(PRESS) |  |  | 75%

###  3.13 Dermatology

The diagnosis of dermatological diseases using machine learning and deep
learning was proposed by [50]. Three machine learning models, namely neural
networks, decision tree, and an nth nearest neighbor were built and trained
using a standard dataset. An approach was performed by [8] to diagnose skin
diseases from colored images using artificial neural networks. Various
methods, like image cropping and color gradient generation, are applied to
preprocess the data. A feedforward neural network with a backpropagation
algorithm was used for diagnosis. The segmentation of skin ulcer images using
machine learning was proposed by [97]. In this study, several models like
Extreme learning machine, J48, Support vector machine, K-nearest neighbor,
Random Forest, Multilayer perceptron, and Naive Bayes were analyzed. The
performance of these algorithms in the segmentation of ulcers is shown in
table 14. A survey was performed by [37] on using machine learning algorithms
for classifying skin diseases based on color and texture features. For this
survey, skin diseases, namely Eczema, Lichen plants, and plague psoriasis,
were diagnosed using linear discriminant analysis, artificial neural networks,
naive Bayes, and support vector machine. A novel method was proposed by [51]
to diagnose erythematosquamous diseases using ensemble modeling. Support
vector machine, multilayer perceptron, and k-nearest neighbor algorithms were
built using the features extracted based on rough set theory. The results of
these algorithms were combined using the majority voting method(Ensemble
method). The diagnosis of diseases, namely melanocytes nevus, seborrheic
keratosis, basal cell carcinoma, and psoriasis using neural network approaches
were proposed by [133]. In this approach, transfer learning of GoogleNet
InceptionV3 is used for diagnosis. For this approach, two different datasets
with the same diseases and features were used to train the model. The early
diagnosis of skin tumors is possible by estimating the constituent elements of
the skin. An approach was proposed by [120] to estimate the skin elements
using machine learning. The support vector regressor model was used for
estimating skin constituents.

Table 14: ML and DL methods in Dermatology.

Author | Method | Application/Diagnosis | Metrics | Findings
---|---|---|---|---
[50] | Decision Tree |  | Psoriasis, seborrheic
---
dermatitis, lichen planus,
pityriasis rosea, chronic
dermatitis, pityriasis rubra
pilaris
Accuracy | 95%
| Neural Networks |  |  | 95%
| Kth Nearest Neighbour |  |  | 95%
[8] |  | feed-forward
---
back-propagation ANN
| Acne, eczema, psoriasis,
---
tinea corporis, scabies and
vitiligo
Accuracy | 95.99% for diseases skin detection
|  |  |  | 94.016% for disease identification
[97] | Naive Bayes | Skin ulcer | Accuracy |  | full regions recognition: 0.64
---
Border Region Recognition: 0.67
Center Region Recognition: 0.65
| K-Nearest Neighbour |  |  |  | full regions recognition: 0.93
---
Border regions recognition: 0.71
Center Region Recognition: 0.95
| ELM |  |  |  | full regions recognition: 0.87
---
Border regions recognition: 0.67
Center Region Recognition: 0.88
| Support Vector Machine |  |  |  | full regions recognition: 0.94
---
Border regions recognition: 0.71
Center Region Recognition: 0.95
| J48 |  |  |  | full regions recognition: 0.92
---
Border regions recognition: 0.69
Center Region Recognition: 0.93
| Naive Bayes |  |  |  | full regions recognition: 0.64
---
Border Region Recognition: 0.67
Center Region Recognition: 0.65
| Random Forest |  |  |  | full regions recognition: 0.93
---
Border regions recognition: 0.71
Center Region Recognition: 0.95
| Multi-Layer Perceptron |  |  |  | full regions recognition: 0.94
---
Border regions recognition: 0.70
Center Region Recognition: 0.95
[37] | Artificial Neural Network |  | Eczema, Lichen plants,
---
and plague psoriasis
Accuracy | 62.9
|  |  | Standard Deviation | 33.11
| Linear Discriminant Analysis |  | Accuracy | 80.9
|  |  | Standard Deviation | 23.62
| Naive Bayes |  | Accuracy | 67.42
|  |  | Standard Deviation | 21.84
| Support Vector Machine |  | Accuracy | 81.61
|  |  | Standard Deviation | 22.87
[51] |  | Majority voting of Multilayer
---
perceptron, K-Nearest
Neighbour and Support
Vector Machine
Erythematosquamous | Accuracy | 97.78%
[133] |  | GoogleNet Inception v3
---
with transfer learning
| Melanocytes nevus,
---
seborrheic keratosis, basal
cell carcinoma, and psoriasis
Average accuracy | 86.54%
|  |  | Standard deviation | 3.63%

###  3.14 Hepatology

A comparative study of using machine learning techniques to diagnose the
advanced stage of liver fibrosis in patients suffering from Hepatitis C was
performed by [36]. In this study, five machine learning algorithms were
analyzed, namely alternating decision tree, particle swarm optimization,
multi-linear regression, alternating decision tree with criterion point zero,
and genetic algorithm. An approach was proposed by [73] to diagnose liver
fibrosis from liver biopsies using machine learning and search algorithms. In
this approach, Related Objects, Skipper algorithm is used to query the
database, and a rule-based subsumption algorithm is used for diagnosis.
Predicting the stage of liver fibrosis is essential for the treatment of the
disease. [11] proposed a methodology to predict the stage of liver fibrosis
using machine learning. In this methodology, liver fibrosis is classified into
five stages using a decision tree algorithm. A methodology was proposed by
[16] for predicting the stage of fibrosis and inflammatory activity from serum
indices data of Hepatitis C patients using the Extreme learning machine
algorithm.

Table 15: ML and DL methods in Hepatology.

Author | Method | Diagnosis/Application | Metric | Findings
---|---|---|---|---
[36] | Multi-linear regression | Liver fibrosis | Accuracy | 69.1%
|  |  | Sensitivity | 69.0%
|  |  | Specificity | 69.1%
| Genetic Algorithms |  | Accuracy | 69.6%
|  |  | Sensitivity | 68.9%
|  |  | Specificity | 69.7%
| Alternating decision tree |  | Accuracy | 66.3%
|  |  | Sensitivity | 73.0%
|  |  | Specificity | 65.0%
| Particle Swarm Optimization |  | Accuracy | 66.4%
|  |  | Sensitivity | 70.4%
|  |  | Specificity | 65.6%
|  | Alternating Decision Tree
---
model with criteria point
of zero
| Accuracy | 84.4%
|  |  | Sensitivity | 70%
|  |  | Specificity | 99%
[73] |  | Related Objects Skipper
---
algorithm with
Subsumptions Rule-Based
Classifier
Liver fibrosis | Accuracy | 99.48%
[11] | Decision Tree Classifier | Liver fibrosis | Accuracy | 93.7%
[16] | Extreme Learning machine |  | Liver fibrosis and
---
inflammatory activity
Accuracy |  | 69.11%
---
(fibrosis stage)
|  |  |  |  | 69.92%
---
(inflammatory activity)

###  3.15 Nephrology

The prediction of chronic kidney disease using machine learning was proposed
by [19]. For this work, four machine learning algorithms, namely support
vector machine, decision tree, k-nearest neighbor, and logistic regression,
were explored to find the best classifier. The support vector machine was
found to be the best classifier. A methodology for the diagnosis of chronic
kidney disease using a support vector machine was proposed by [120]. For this
work, the features are selected based on the standard deviation of data. A
model was proposed by [124] to suggest the best diet plan for chronic kidney
disease patients using machine learning. The best diet plan is recommended by
this system by predicting the potassium zone of the patient. This experiment
is carried out by various multi-class classifiers, namely decision tree,
decision jungle, logistic regression, and neural network. Better performance
was found in the decision tree. The surveyed models are tabulated in Table 16.

Table 16: ML and DL methods in Nephrology.

Author | Method | Diagnosis/Application | Metric | Findings
---|---|---|---|---
[19] | Support Vector Machine | Chronic kidney disease | Accuracy | 98.3%
|  |  | Sensitivity | 99%
|  |  | Specificity | 98%
| Logistic Regression |  | Accuracy | 96.55%
|  |  | Sensitivity | 94%
|  |  | Specificity | 98%
| Decision Tree |  | Accuracy | 94.8%
|  |  | Sensitivity | 93%
|  |  | Specificity | 96%
| K-Nearest Neighbour |  | Accuracy | 98.1%
|  |  | Sensitivity | 96%
|  |  | Specificity | 99%
[20] | Support Vector Machine | Chronic kidney disease | Accuracy | 82%
[124] |  | Multiclass Logistic
---
Regression
Diet plan for chronic kidney disease | Accuracy | 89.17%
|  | Multiclass Neural
---
Network
|  | 82.50%
| Multiclass Decision Forest |  |  | 99.17%
|  | Multiclass Decision
---
Jungle
|  | 97.50%

###  3.16 Ophthalmology

[35] and [114] performed a study on using a convolutional network to detect
ophthalmological diseases like cataract, glaucoma, and retinal disorders.
Various models, namely decision tree, neural network, Naive Bayes, and random
forest, were analyzed by [62] for detecting eye diseases. The performances of
these algorithms are shown in table 18. From these algorithms, random forest
and decision tree showed better performance. Vision impairing diseases like
diabetic eye disease are major problems in today’s world. The diagnosis of
diabetic eye disease from thermographic images using machine learning is
proposed by [99]. For this work, texture-based features are used by the
support vector machine classifier for diagnosis. Exudates and hemorrhages are
the two primary causes of diabetic retinopathy. The diagnosis of diabetic
retinopathy from retinal scans was proposed by [95]. For this approach,
feature extraction and detection of hard exudates and hemorrhages from retinal
images are done using principal component analysis and support vector machine.
[24] proposed a method to detect Diabetic retinopathy and glaucoma from
retinal images using a neural network framework. This work focuses on changing
parametric values of the VGG 19 neural network for an accurate diagnosis.

Table 17: ML and DL methods in Ophthalmology. Author | Method | Diagnosis/Application | Metrics | Findings
---|---|---|---|---
[62] | Decision Tree |  Glaucoma, Unspecified primary angle-closure glaucoma | Accuracy | 85.81%
Random Forest | 86.63%
Naive Bayes | 81.53%
Neural Network | 85.98%
[99] | Support vector machine | Diabetic eye disease | Accuracy | 86.22%
Sensitivity | 94.07%
Specificity | 79.17%
[95] | Support Vector machine | Diabetic retinopathy | Accuracy | 96%
Sensitivity | 94%

###  3.17 Drug discovery

This section deals with the application of machine learning algorithms in the
Pharma industry. [79] used machine learning for drug design. For this work,
the prediction of inhibition of dihydrofolate reductase from pyrimidines is
made using a support vector machine. The data for this research is taken from
the University of California Irvine(UCI) machine learning repository. In this
paper, the performance of Support Vector Machine is compared with the Prune
network, multilayer perceptron(MLP) feedforward network, radial bias function
network(RBF), dynamic network, and C5.0. The support vector machine performed
best among the above algorithms with a training time of 93 seconds. Machine
learning is used by [86] to predict proximal tubular toxicity in humans. For
this work, the researchers developed a one-step protocol to differentiate
human induced pluripotent stem cells(hiPCS) into proximal tubular like cells.
A random forest algorithm is used for prediction. This algorithm is evaluated
based on sensitivity, specificity, and accuracy and is shown in table 18. Drug
likeliness is predicted using machine learning by [47]. In this work, Bayesian
classifier, recursive portioning models were developed to predict the drug
likeliness, and these models are evaluated based on the compounds from the
Traditional Chinese medicine compound database(TCMCD). Bayesian classifier
with the LCFP_6 fingerprint set and physicochemical properties outperformed
the Recursive partitioning model. A study was performed by [118] on using
machine learning and deep learning techniques in drug discovey. In this paper
the authors surveyed the different machine learning and deep learning models
that were used in target identification and validation, design and
optimization of small molecules, discovery of biomarkers, prediction of drug
sensitivity and in computational pathology.

Table 18: ML and DL methods in Drug Discovery. Author | Method | Application | Metrics | Findings
---|---|---|---|---
[79] | SVM | Drug design | Error | 0.1269
[86] | Random Forest | Proximal tubular toxicity | Sensitivity | 89.0%
|  |  | Specificity | 85.0%
|  |  | Accuracy | 87.0%
[47] | Naive Bayesian Classifier | Drug likeliness |  | Accuracy
---
(test set)
90.9%
|  |  |  | Accuracy
---
(training set)
91.4%

###  3.18 COVID-19

ML and DL models play a prominent role in current pandemic situation,
COVID-19. Researchers have done a extensive use of ML and DL methods for
helping the healthcare professionals in diagnosing the disease through
multiple sources ranging from X-rays till individual’s comorbities. The
authors have done a thorough literature study and the results obtained are
shown in table 19. [38] proposed a deep learning model named COVIDX-Net
consisting of seven convolutional neural networks(CNN). This COVIDX-Net model
diagnosed COVID-19 infection from x-ray images with an F1-score of 0.91. A
deep learning open source model named COVID-Net was proposed by [122] to
diagnose COVID-19. This model was able to classify the x-ray images into three
classes namely normal, Pneumonia, and COVID-19 with an accuracy of 92$\%$. [7]
performed a comparative study on using state-of-the-art deep learning
architectures namely VGG19, MobileNet, Xception, Inception, and Inception
ResNet v2 for the diagnosis of COVID-19. According to this study, VGG19 and
MobileNet showed enhanced performance compared to the other models with an
accuracy of 98.75$\%$ and 97.40$\%$. Another comparative study was performed
by [72] on using ResNet50, ResNet152, ResNet101, Inception-ResNetV2 and
InceptionV3 to diagnose COVID-19 from chest x-ray images of three different
datasets. According to this study, it was observed that ResNet50 performed
better than other models with the accuracies of 96.1$\%$, 99.5$\%$, and
99.7$\%$ on all the three datasets. [100] diagnosed COVID-19 from x-ray images
using a support vector machine trained with features extracted by the ResNet50
model. [106] developed a deep learning-based diagnosis system known as
DeepPneumonia to diagnose COVID-19 and bacterial pneumonia. In this work, the
authors used data collected from 86 healthy persons, 101 patients diagnosed
with bacterial pneumonia, and 88 patients diagnosed with COVID-19. This data
was collected from 19 hospitals in China. This model was tested with images
from two datasets. [134] developed a weakly-supervised deep learning model for
the diagnosis of COVID-19 from chest CT images. In this work, a pre-trained
U-Net architecture is used to segment the lung region from the image and a 3D
deep neural network was used to diagnose the COVID-19 infection in the lungs.
This system is very fast at it takes only 1.93s to process a CT image of a
patient and diagnoses the disease with an accuracy of 90.1$\%$ and specificity
of 91.1$\%$. [129] proposed a methodology to diagnose COVID-19 pneumonia and
Influenza-A viral pneumonia. In this methodology, a 3D CNN model is used to
segment the candidate regions in the lungs and a location-attention classifier
is used to classify these regions into COVID-19 pneumonia, influenza-A viral
pneumonia and as normal and finally, a noisy or Bayesian function is used to
calculate the infection probability. [13] performed a study on using a support
vector machine as a classifier trained with features extracted using five
different algorithms for the diagnosis of COVID-19 CT images. The five feature
extraction algorithms that were used in this work are Local Directional
Pattern(LDP), Grey Level Co-occurrence Matrix(GLCM), Grey-Level Size Zone
Matrix (GLSZM), Grey Level Run Length Matrix (GLRLM), and Discrete Wavelet
Transform (DWT). According to this study support vector machine trained with
features extracted using the GLSZM algorithm performed better than other
feature extraction algorithms with an accuracy of 99.68$\%$. [61] proposed a
methodology to diagnose COVID-19 using multi-level thresholding and a support
vector machine. [21] designed a deep learning model using transfer learning
UNet for the diagnosis of COVID-19. For this work, 46,096 anonymous CT images
are collected from 106 patients admitted to Renmin hospital in Wuhan city were
used, out of which 51 patients are diagnosed with COVID-19. [81] proposed a
transfer learning model by fine-tuning the pre-trained model VGG16 for the
diagnosis of COVID-19. [90] compared two deep learning models that were built
using the transfer learning of residual networks ResNet34 and ResNet50 for the
diagnosis of pneumonia and COVID-19. According to this paper, ResNet50
performed better than ResNet32 with an accuracy of 72.38$\%$. [103] proposed a
deep learning model that is designed using VGG16 and a faster R-CNN framework
to diagnose COVID-19.

Table 19: ML and DL methods for the diagnosis of COVID-19. Author | Method | Disease | Metrics | Findings
---|---|---|---|---
[38] |  | Ensemble of 7
---
convolutional
neural networks
COVID-19 | f1-score | 0.91
[122] | Deep CNN |  | Pneumonia and
---
COVID-19
Accuracy | 92%
[100] |  | Support vector
---
machine trained
with features
extracted by VGG16
COVID-19 | Accuracy | 95.38%
[106] | Deep CNN |  | COVID-19 and
---
bacterial pneumonia
Accuracy | 86%
|  |  | Sensitivity | 96%
[128] |  | Transfer learning
---
of InceptionV3
| COVID-19 and viral
---
pneumonia
Accuracy | 89.5%
[134] |  | U-Net based
---
segmentation and
weakly supervised
learning based
classifier
COVID-19 | Accuracy | 90.1%
[129] |  | 3D CNN based
---
segmentation with
attention based classifier
| COVID-19 pneumonia
---
and Influenza A viral
Pneumonia
Accuracy | 86.7%
[61] |  | Multi-level thresholding
---
and SVM
COVID-19 | Accuracy | 97.84%
|  |  | Sensitivity | 95.76%
|  |  | Specificity | 99.7%
[21] |  | Transfer learning of
---
U-Net
COVID-19 | Accuracy | 98.85%
[81] |  | Transfer learning by
---
fine tuning VGG16
COVID-19 | Accuracy | 96%
[103] |  | CNN designed using
---
VGG16 and Faster
RCNN framework
COVID-19 | Accuracy | 97.36%
|  |  | Sensitivity | 97.65%
|  |  | Precision | 99.28%

##  4 Discussion

###  4.1 Smart Healthcare Systems using ML and DL

The usage of ML and DL models created a massive impact across all domains
especially, the healthcare industry. Even with the advent of many reports and
scans, human decision making is the only way for diagnosing diseases. This may
sometimes lead to unreliable diagnosis due to bias in human decisions. Smart
intelligent techniques like ML and DL methodologies can be used to improve
reliability in diagnosis thereby saving lot of human lives. Thus researchers
started proposing various models for automating diagnosis in healthcare in
various specializations including Orthopedics( estimating bone density,
diagnosing rheumatoid arthritis), Radiology( interpreting mammography,
computed tomography and magnetic resonance imaging scans), Ophthalmology(
diagnosis of diabetic retinopathy, cataract, glaucoma), Cardiology( diagnosis
of complex diseases like hypertrophic cardiomyopathy) and many more. Such
diagnosis may help saving lives of human. For eg., according to a study
performed by the American Cancer Society [28] it was found that more than
50$\%$ of the women who are getting annual mammograms over 10 years are having
false-positive findings. These false-positive findings in mammograms can lead
to unnecessary examinations like ultrasound scans, MRI, and even biopsies and
this can be avoided by using ML and DL based systems.

###  4.2 Impact of Computerized diagnostic systems on physicians

Machine learning and deep learning are playing a key role in all stages of
drug discovery. These techniques are used extensively for better understanding
of disease mechanisms, non-disease and disease phenotypes, to identify novel
targets and in development of biomarkers for drug efficiency and prognosis.
These systems are becoming more effective than medical professionals in terms
of diagnosis and predictive analysis of diseases [89, 57]. This led to a major
concern that these systems will replace medical professionals in near future,
especially radiologists. The main reason for this concern is its ability to
learn from millions of images and this learning enables the computer systems
to diagnose the most complex diseases that are difficult to be diagnosed by
experienced medical professionals. But there are few limitations with these
systems, so we can’t completely depend on these systems for diagnosis. But
these systems can be a part of a radiologist’s life, by assisting them during
diagnosis and making their work more accurate and efficient.

In the case of other medical specialties, computer systems cannot be a
replacement for doctors because these systems cannot gain trust in patients
and cannot have high-level interactions with the patients. Even though one
might say that there is a possibility that these systems can make clinical
conversations in the future. But, even if computer systems are built to an
extent that they can conduct real-time MRI, CT, and other imaging examinations
and perform automated surgeries, they cannot replace doctors. Doctors are
still needed for the diagnosis and treatment of novel and ambiguous cases.
These computer systems under-perform in case of novel diseases, side effects
caused by drugs because there is no prior instance available for these systems
to learn from [30]. For these reasons, it can be concluded the smart
intelligent computer systems will support doctors by assisting them during the
diagnosis and treatment of diseases instead of replacing them.

##  5 Conclusion

The diagnosis of diseases is a complex and time consuming process. The
conventional procedures followed to diagnose diseases are not so accurate.
Intelligent computer systems are being used by medical professionals for fast
and accurate diagnosis of diseases. There is a lot of research that is done in
this field. So the main aim of this paper is to present a comprehensive survey
of different machine learning and deep learning models that are developed and
used in building computer systems to diagnose diseases across various
specializations of medicine. Along with the survey, we discuss the
advancements in healthcare with machine learning and deep learning-based
systems and also the impacts of these systems on medical professionals. This
paper provides a deep dive into the different frameworks, models and tools
along with practical concerns and considerations in Health care domain that
include Dental medicine, Haematology, Surgery, Cardiology, Pulmonology,
Orthopedics, Radiology, Oncology, General medicine, Psychiatry, Endocrinology,
Neurology, Dermatology, Hepatology, Nephrology, and Ophthalmology. Along with
the medical specializations mentioned above, we also present the survey of
machine learning and deep learning models in drug discovery.

###  5.1 Highlights of the work

  * 1.

Exploring prominent research works in designing the various robust Intelligent
health care systems using ML and DL techniques for early diagnosis of
diseases.

  * 2.

Examining constructive research outputs demonstrating a promising
supplementary diagnostic method for frontline clinical doctors and surgeons.

  * 3.

Thorough analysis of applications of ML and DL across 16 medical specialties,
namely Dental medicine, Haematology, Surgery, Cardiology, Pulmonology,
Orthopaedics, Radiology, Oncology, General medicine, Psychiatry,
Endocrinology, Neurology, Dermatology, Hepatology, Nephrology, Ophthalmology,
and Drug discovery.

  * 4.

Deep dive into the different ML and DL frameworks, models and tools along with
practical concerns, challenges and considerations in Health care domain.

  * 5.

Investigating the use of ML and DL mechanisms in predicting COVID-19, the
deadly and infectious disease.

  * 6.

Look into the impact of Computerized diagnostic systems on physicians with a
promising discussion on future research deliberations.

  * 7.

Recording relevant list of Public Data sets available for future works.

##  6 Suggestions for future work

  * 1.

Fuzzy based diagnosis systems can be built to diagnose complex diseases like
cancers, heart attacks, and diabetic retinopathy.

  * 2.

Building Neural network-based systems to diagnose diseases from image-based
data like X-rays, CT, and MRI scans.

  * 3.

Transfer learning models can be built to diagnose diseases that are difficult
to detect, like novel fevers, celiac disease, brain, and ovarian cancers,
diabetic eye disease, and glaucoma.

  * 4.

Designing systems with machine learning and Internet of Things(IoT) techniques
for the evaluation of organs and for diagnosis of Haematological diseases from
blood samples.

  * 5.

Developing a pattern recognition based model for the diagnosis of
Cardiovascular and Pulmonary diseases.

  * 6.

Building hybrid systems with image recognition approaches for the staging of
various cancers, for diagnosis of neurological diseases like brain hemorrhage
and hydrocephalus.

  * 7.

Developing robots and Surgeon-assisting tools with machine learning techniques
to perform robotic and minimally invasive surgeries.

  * 8.

Building a system that estimates the stress levels and suggests lifestyle
changes that are needed to reduce stress.

  * 9.

Building systems with a different combination of algorithms using ensemble
methods for diagnosing diseases.

##  7 List of abbreviations

AI - Artificial Intelligence
ML - Machine learning
DL - Deep learning
MRI - Magnetic Resonance Imaging
RVG - Radio Videography
CNN - Convolutional Neural Network
UCI - University of California
MLP - Multi Layer Perceptron
RBF - Radial Bias Function
hiPCS - Human induced pluripotent stem cells
TCMCD - Traditional Chinese medicine compound database
ECG - Electrocardiogram
ReLU - Rectified Linear Unit
HCM - Hypertrophic cardiomyopathy
SPET - Single-photon emission computed tomography
MACE - major adverse cardiac events
ARDS - Acute diseases like Respiratory distress syndrome
ROC - Receiver operating characteristic
EDR - Electrocardiogram derived respiratory
CT - Computed tomography
JSN - Joint space narrowing
SWI - Susceptibility weighted imaging
LASIK - Laser-Assisted In-Situ Keratomileusis
IoT - Internet of Things
SVM - Support Vector Machine
KNN - K-Nearest Neighbor
LMLA - Levenberg Marquardt Learning Algorithm
DWNN - Distance-weighted -nearest-neighbor
ELM - Extreme Learning Machine
RVM - Relvence Vector Machine
LVQ - Linear Vector Quantization
LR - Logistic Regression
GA - Genetic Algorithm
GAN - Generative adversarial neural networks

##  8 List of public datasets

  * 1.

ChestXpert
https://stanfordmlgroup.github.io/competitions/chexpert/

  * 2.

ChestXray-NIHCC
https://nihcc.app.box.com/v/ChestXray-NIHCC

  * 3.

MIMIC-CXR
https://physionet.org/physiobank/database/mimiccxr/

  * 4.

PadChest
http://bimcv.cipf.es/bimcv-projects/padchest/

  * 5.

IBM Xray Eye Gaze
https://physionet.org/content/egd-cxr/1.0.0/

  * 6.

Cancer Image Archive
http://www.cancerimagingarchive.net/

  * 7.

National Lung Screening Trial
https://wiki.cancerimagingarchive.net/display/NLST/
National+Lung+Screening+Trial

  * 8.

DeepLesion
https://nihcc.app.box.com/v/DeepLesion

  * 9.

EchoNet-Dynamic
https://echonet.github.io/dynamic/

  * 10.

ABCD Neurocognitive Prediction Challenge
https://sibis.sri.com/abcd-np-challenge/

  * 11.

AAPM Sparse-View CT Reconstruction Challenge
https://www.aapm.org/GrandChallenge/DL-sparse-view-CT/

  * 12.

Cross-Sectional Multidomain Lexical Processing
https://openneuro.org/datasets/ds002236

  * 13.

Neurite-OASIS
https://github.com/adalca/medical-datasets/blob/master/neurite-oasis.md

  * 14.

MRNet
https://stanfordmlgroup.github.io/competitions/mrnet/

  * 15.

fastMRI
https://fastmri.med.nyu.edu/

  * 16.

OCMR
https://ocmr.info/

  * 17.

PREVENT-AD
https://openpreventad.loris.ca/

  * 18.

Medical Segmentation Decathlon
http://medicaldecathlon.com/

  * 19.

MASSIVE
http://massive-data.org/download.html

  * 20.

AOMIC: the Amsterdam Open MRI Collection
https://nilab-uva.github.io/AOMIC.github.io/

  * 21.

MRIdata
http://mridata.org/

  * 22.

Brain MRI LGG FLAIR abnormality segmentation
https://www.kaggle.com/mateuszbuda/lgg-mri-segmentation

  * 23.

Studyforrest
http://studyforrest.org/data.html

  * 24.

Lung Image Database Consortium
https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI

  * 25.

UK Biobank
https://biobank.ctsu.ox.ac.uk/crystal/download.cgi

  * 26.

BrixIA: COVID19 severity score assessment databse
https://brixia.github.io/

  * 27.

COVID-CT
https://github.com/UCSD-AI4H/COVID-CT

  * 28.

Medical Imaging Data Resource Center (MIDRC)
https://wiki.cancerimagingarchive.net/pages/
viewpage.action?pageId=70230281

  * 29.

BIMCV-COVID19
http://bimcv.cipf.es/bimcv-projects/bimcv-covid19/

  * 30.

MosMedData Covid19
https://mosmed.ai/en/

  * 31.

COVID-19 LUNG CT LESION SEGMENTATION CHALLENGE
https://covid-segmentation.grand-challenge.org/Data/

  * 32.

MedSeg COVID-19 CT
http://medicalsegmentation.com/covid19/

  * 33.

COVID-Chest XRay
https://github.com/ieee8023/covid-chestxray-dataset

  * 34.

BSTI COVID19
https://bsticovid19.cimar.co.uk/worklist/?embedded=

  * 35.

RICORD
https://www.rsna.org/covid-19/COVID-19-RICORD/RICORD-resources

  * 36.

FIRE (Fundus Image Registration Dataset)
https://paperswithcode.com/dataset/fire

  * 37.

DRIVE: Digital Retinal Images for Vessel Extraction
https://drive.grand-challenge.org/

  * 38.

FLARE: Fast and Low GPU memory Abdominal Organ Segmentation
https://flare.grand-challenge.org/

  * 39.

Diabetes
https://archive.ics.uci.edu/ml/datasets/Diabetes

  * 40.

Thyroid Disease
https://archive.ics.uci.edu/ml/datasets/Thyroid+Disease

  * 41.

Breast cancer
https://archive.ics.uci.edu/ml/datasets/Breast+Cancer

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