# Machine learning based disease diagnosis: A comprehensive review

Md Manjurul Ahsan
Industrial and Systems Engineering
University of Oklahoma
Norman, Oklahoma-73071
<EMAIL_ADDRESS>
Zahed Siddique
Department of Aerospace and Mechanical Engineering
University of Oklahoma
Norman, Oklahoma-73071
<EMAIL_ADDRESS>
###### Abstract

Globally, there is a substantial unmet need to diagnose various diseases
effectively. The complexity of the different disease mechanisms and underlying
symptoms of the patient population presents massive challenges to developing
the early diagnosis tool and effective treatment. Machine Learning (ML), an
area of Artificial Intelligence (AI), enables researchers, physicians, and
patients to solve some of these issues. Based on relevant research, this
review explains how Machine Learning (ML) and Deep Learning (DL) are being
used to help in the early identification of numerous diseases. To begin, a
bibliometric study of the publication is given using data from the Scopus and
Web of Science (WOS) databases. The bibliometric study of 1216 publications
was undertaken to determine the most prolific authors, nations, organizations,
and most cited articles. The review then summarizes the most recent trends and
approaches in Machine Learning-based Disease Diagnosis (MLBDD), considering
the following factors: algorithm, disease types, data type, application, and
evaluation metrics. Finally, the paper highlights key results and provides
insight into future trends and opportunities in the MLBDD area.

_K_ eywords Alzheimer’s disease $\cdot$ Artificial neural networks $\cdot$
Breast cancer $\cdot$ Convolutional neural networks $\cdot$ COVID-19 $\cdot$
Decision trees $\cdot$ Deep learning $\cdot$ Deep neural networks $\cdot$
Diabetes $\cdot$ Disease diagnosis $\cdot$ Disease prediction $\cdot$ Heart
disease $\cdot$ Kidney disease $\cdot$ K-means clustering $\cdot$ K-nearest
neighbors $\cdot$ Machine learning $\cdot$ Neural networks $\cdot$ Parkinson
disease $\cdot$ Random forest $\cdot$ Review $\cdot$ Sars-COVID-2 $\cdot$
Support vector machine

## Abbreviation

ANN Artificial Neural Network
CNN Convolutional Neural Network
DL Deep Learning
DNN Deep Neural Networks
DT Decision Trees
KMC K-Means Clustering
KNN K-Nearest Neighbors
LR Logistic Regression
ML Machine Learning
NN Neural Network
RF Random Forest
SVM Support Vector Machine

##  1 Introduction

Diagnosis is a branch of Artificial Intelligence (AI) focused with developing
the algorithms and techniques capable of determining whether a system’s
behavior is correct. Medical diagnosis identifies the disease or conditions
that explain a person’s symptoms and signs. Typically, diagnostic information
is gathered from the patient’s history and physical examination [1]. It is
frequently difficult due to the fact that many indications and symptoms are
ambiguous and can only be diagnosed by trained health experts. Therefore,
countries that lack enough health professionals for their populations, such as
developing countries like Bangladesh and India, face difficulty providing
proper diagnostic procedures for their maximum patients [2]. Moreover,
diagnosis procedures often require medical tests, which low-income people
often find expensive and difficult to afford.
As humans are prone to error, it is not surprising that a patient may be over-
diagnosed more often. If a person is over-diagnosed, a problem like
unnecessary treatments will arise, harming individuals’ health and economic
waste [3]. According to the National Academics of Science, Engineering, and
Medicine report of 2015, the majority of people will encounter at least one
diagnostic mistake during their lifespan [4].Various factors may influence the
misdiagnosis, which includes:

  * •

lack of proper symptoms, which often unnoticeable

  * •

the condition of rare disease

  * •

the disease is omitted mistakenly from the consideration

Machine Learning (ML) is used practically everywhere, from cutting-edge
technology (such as mobile phones, computers, and robotics) to health care
(i.e., disease diagnosis, safety). ML is gaining traction in various fields,
including disease diagnosis in health care. Many researchers and practitioners
illustrate the promise of Machine Learning-based Disease Diagnosis (MLBDD),
which is inexpensive and time-efficient [5]. Traditional diagnosis processes
are costly, time-consuming, and often require human intervention. While the
individual’s ability restricts traditional diagnosis techniques, ML-based
systems have no such limitations, and machines do not get exhausted as humans
do. As a result, a method to diagnose disease with outnumbered patients’
unexpected presence in health care may be developed. To create MLBDD systems,
health care data like images (i.e., X-ray, MRI) and tabular data (i.e.,
patients’ conditions, age, and gender) are employed [6].
Machine Learning (ML) is a subset of AI that uses data as an input resource
[7]. The use of predetermined mathematical functions yields a result
(classification or regression) that is frequently difficult for humans to
accomplish. For example, using ML, locating malignant cells in a microscopic
image is frequently simpler, which is typically challenging to conduct just by
looking at the images. Furthermore, since advances in Deep Learning (a form of
machine learning), the most current study shows MLBDD accuracy of above 90%
[5]. Alzheimer’s disease, Heart failure, Breast cancer, and Pneumonia are just
a few of the diseases that may be identified with ML. The emergence of Machine
Learning (ML) algorithms in disease diagnosis domains illustrates the
technology’s utility in medical fields.
Recent breakthroughs in ML difficulties, such as unbalanced data, ML
interpretation, and ML ethics in medical domains, are only a few of the many
challenging fields to handle in a nutshell [8]. This paper gives a review that
highlights the novel uses of ML and DL in disease diagnosis and provides an
overview of development in this field in order to shed some light on this
current trend, approaches, and issues connected with ML in disease diagnosis.
It begins by outlining several methods to Machine Learning and Deep Learning
techniques, and particular architecture for detecting and categorizing various
forms of disease diagnosis.

###  1.1 Motivation

The purpose of this review is to provide insights to recent and future
researchers and practitioners regarding Machine Learning-based Disease
Diagnosis (MLBDD) that will aid and enable them to choose the most appropriate
and superior Machine Learning/Deep Learning methods, thereby increasing the
likelihood of rapid and reliable disease detection and classification in
diagnosis. Additionally, the review aims at identifying potential studies
related to the MLBDD. In general, the scope of this study is to provide the
proper explanation for the following questions:

  1. 1.

What are some of the diseases that researchers and practitioners are
particularly interested in when evaluating data-driven machine learning
approaches?

  2. 2.

Which databases are incorporated into MLBDD?

  3. 3.

Which Machine Learning and Deep Learning approaches are presently used in
health care to classify various forms of disease?

  4. 4.

Which architecture of Convolutional Neural Networks (CNNs) is widely employed
in disease diagnosis?

  5. 5.

How is the model’s performance evaluated? Is that sufficient?

This paper summarizes the different Machine Learning (ML) and Deep Learning
(DL) methods utilized in various disease diagnosis applications. The remainder
of the paper is structured as follows: Section 2 discusses the background and
overview of ML and DL, whereas Section 3 details the article selection
technique. Section 4 includes bibliometric analysis. Section 5 discusses the
use of Machine Learning in various disease diagnoses, and Section 6 identifies
the most frequently utilized ML methods and datatypes based on the linked
research. Section 7 discusses the findings, anticipated trends, and problems.
Finally, Section 8 concludes the article with a general conclusion.

##  2 Basics and Background

Machine Learning (ML) is an approach that analyzes data samples to create main
conclusions using mathematical and statistical approaches, allowing machines
to learn without programming. Arthur Samuel presented machine learning in
games and pattern recognition algorithms to learn from experience in 1959,
which was the first time the important advancement was recognized. The core
principle of ML is to learn from data in order to forecast or make decisions
depending on the assigned task [9]. Thanks to Machine Learning (ML)
technology, many time-consuming jobs may now be completed swiftly and with
minimal effort. With the exponential expansion of computer power and data
capacity, it is becoming simpler to train data-driven machine learning models
to predict outcomes with near-perfect accuracy. Several papers offer various
sorts of ML approaches [10, 11].
The ML algorithms are generally classified into three categories such as
supervised, unsupervised, and semi-supervised [10]. However, ML algorithms can
be divided into several subgroups based on different learning approaches, as
shown in Fig. 1. Some of the popular ML algorithms include Linear Regression,
Logistic Regression, Support Vector Machines (SVM), Random Forest (RF), and
Naïve Bayes (NB) [10].

Figure 1: Different types of Machine Learning algorithms.

###  2.1 Machine Learning Algorithms

This section provides a comprehensive review of the most frequently used
Machine Learning algorithms in disease diagnosis.

####  2.1.1 Decision Tree

The Decision Tree (DT) algorithm follows divide and conquer rules. In DT
models, the attribute may take on various values known as classification
trees; leaves indicate distinct classes, while branches reflect the
combination of characteristics that result in those class labels. On the other
hand, DT can take continuous variables called regression trees. C4.5 and EC4.5
are the two famous and most widely used DT algorithms [12]. DT is used
extensively by following reference literature: [13, 14, 15, 16].

####  2.1.2 Support Vector Machine

For classification and regression-related challenges, Support Vector Machine
(SVM) is a popular ML approach. SVM was introduced by Vapnik in the late 20th
century [17]. Apart from disease diagnosis, SVMs have been extensively
employed in various other disciplines, including facial expression
recognition, protein fold, distant homology discovery, speech recognition, and
text classification. For unlabeled data, supervised ML algorithms are unable
to perform. Using a hyperplane to find the clustering among the data, SVM can
categorize unlabeled data. However, SVM output is not non-linearly separable.
To overcome such problems, selecting appropriate kernel and parameters is two
key factors when applying SVM in data analysis [11].

####  2.1.3 K-Nearest Neighbor

K-Nearest Neighbor (KNN) classification is a non-parametric classification
technique invented in 1951 by Evelyn Fix and Joseph Hodges. KNN is suitable
for classification as well as regression analysis. The outcome of KNN
classification is class membership. Voting mechanisms are used to classify the
item. Euclidean distance techniques are utilized to determine the distance
between two data samples. The projected value in regression analysis is the
average of the values of the KNN [18].

####  2.1.4 Naïve Bayes

The Naive Bayes (NB) classifier is a Bayesian-based probabilistic classifier.
Based on a given record or data point, it forecasts membership probability for
each class. The most probable class is the one having the greatest
probability. Instead of predictions, the NB classifier is used to project
likelihood [11].

####  2.1.5 Logistic Regression

Logistic regression (LR) is an ML approach that is used to solve
classification issues. The LR model has a probabilistic framework, with
projected values ranging from 0 to 1. Examples of LR-based ML include spam
email identification, online fraud transaction detection, and malignant tumor
detection. The cost function, often known as the Sigmoid function, is used by
LR. The Sigmoid function transforms every real number between 0 and 1 [19].

####  2.1.6 AdaBoost

Yoav Freund and Robert Schapire developed Adaptive Boosting, popularly known
as AdaBoost. Adaboost is a classifier that combines multiple weak classifiers
into a single classifier. AdaBoost works by giving greater weight to samples
that are harder to classify and less weight to those that are already well
categorized. It may be used for categorization as well as regression analysis
[20].

###  2.2 Deep Learning Overview

Deep Learning (DL) is a sub-field of Machine Learning (ML) that employs
multiple layers to extract both higher and lower-level information from input
(i.e., images, numerical value, categorical values). The majority of
contemporary DL models are built on Artificial Neural Networks (ANN), notably
Convolutional Neural Networks (CNN), which may be integrated with other DL
models, including generative models, Deep Belief Networks, and the Boltzmann
Machine. Deep Learning may be classified into three types: supervised, semi-
supervised, and unsupervised.. Deep Neural Networks (DNN), Reinforcement
Learning, and Recurrent Neural Networks (RNN) are some of the most prominent
DL architectures (RNN) [21].
Each level in DL learns to convert its input data to the succeeding layers
while learning distinct data attributes. For example, the raw input may be a
pixel matrix in image recognition applications, and the first layers may
detect the image’s edges. On the other hand, the second layer will construct
and encode the nose and eyes, and the third layer may recognize the face by
merging all of the information gathered from the previous two layers [6].
In medical fields, DL has enormous promise. Radiology and pathology are two
well-known medical fields that have widely used DL in disease diagnosis over
the years [22]. Furthermore, collecting valuable information from molecular
state and determining disease progression or therapy sensitivity are practical
uses of DL that are frequently unidentified by human investigations [23].

####  2.2.1 Convolutional Neural Network

Convolutional Neural Networks (CNNs) are a subclass of Artificial Neural
Networks (ANNs) that are extensively used in image processing. CNN is widely
employed in face identification, text analysis, human organ localization, and
biological image detection or recognition [24]. Since the initial development
of CNN in 1989, a different type of CNN has been proposed that has performed
exceptionally well in disease diagnosis over the last three decades. A CNN
architecture comprises three parts: input layer, hidden layer, and output
layer. The intermediate levels of any feed-forward network are known as hidden
layers, and the number of hidden layers varies depending on the type of
architecture. Convolutions are performed in hidden layers, which contain dot
products of the convolution kernel with the input matrix. Each convolutional
layer provides feature maps used as input by the subsequent layers. Following
the concealed layer are more layers, such as pooling and fully connected
layers [21]. Several CNN models have been proposed throughout the years, and
the most extensively used and popular CNN models are shown in Fig. 2.

Figure 2: Some of the most well-known CNN models, along with their development
time frames.

In general, it may be considered that ML and DL have grown substantially
throughout the years. The increased computational capability of computers and
the enormous number of data available inspire academics and practitioners to
employ ML/DL more efficiently. A schematic overview of machine learning and
deep learning algorithms and their development chronology is shown in Fig. 3,
which may be a helpful resource for future researchers and practitioner

Figure 3: Illustration of Machine Learning and Deep Learning algorithms
development timeline.

###  2.3 Performance Evaluations

This section describes the performance measures used in reference literature.
Performance indicators, including accuracy, precision, recall, and f-1 score,
are widely employed in disease diagnosis. For example, lung cancer can be
categorized as true positive ($T_{P}$) or true-negative ($T_{N}$) if
individuals are diagnosed correctly, while it can be categorized into false-
positive ($F_{P}$) or false-negative ($F_{N}$) if misdiagnosed. The most
widely used metrics are described below [10].
Accuracy (Acc): The accuracy denotes total correctly identifying instances
among all of the instances. Accuracy can be calculated using following
formulas:

$ACC=\frac{T_{p}+T_{N}}{T_{p}+T_{N}+F_{p}+F_{N}}$ (1)

Precision ($P_{n}$:) Precision is measured as the proportion of precisely
predicted to all expected positive observations.

$P_{n}=\frac{T_{p}}{T_{p}+F_{p}}$ (2)

Recall ($R_{c}$): The proportion of overall relevant results that the
algorithm properly recognizes is referred to as recall.

$\frac{T_{p}}{T_{n}+F_{p}}$ (3)

Sensitivity ($S_{n}$): Sensitivity denotes only true positive measure
considering total instances and can be measured as follows:

$S_{n}=\frac{T_{p}}{T_{p}+F_{N}}$ (4)

Specificity ($S_{p}$): It identifies how many true negatives are appropriately
identified and calculated as follows:

$S_{p}=\frac{T_{N}}{T_{N}+F_{P}}$ (5)

F- measure: The F1 score is the mean of accuracy and recall in a harmonic
manner. The highest f score is 1, indicating perfect precision and recall
score.

$F-Measure=2\times\frac{\textrm{Precision}\times\textrm{
Recall}}{\textrm{Precision+Recall}}$ (6)

Area under curve (AUC): The area under the curve represents the models’
behaviors in different situations. The AUC can be calculated as follows:

$AUC=\frac{\sum R_{i}(I_{p})-I_{p}((I_{p}+1)/2}{I_{p}+I_{n}}$ (7)

Where $l_{p}$ and $l_{n}$ denotes positive and negative data samples and
$R_{i}$ is the rating of the $i^{th}$ positive samples.

##  3 Article Selection

###  3.1 Identification

The Scopus and Web of Science (WOS) databases are utilized to find original
research publications. Due to their high quality and peer review paper index,
Scopus and WOS are prominent databases for article searching, as many
academics and scholars utilized them for systematic review [25, 26]. Keywords
along with Boolean operator the title search was carried out as follows:
“disease" AND (“diagnsois" OR “Supprot vector machine" OR “SVM" OR “KNN" OR
“K-nearest neighbor" OR “logistic regression" OR “K-means clustering" OR
“random forest" OR “RF" OR “adaboost" OR “XGBoost" , “decision tree" OR
“neural network" OR “NN" OR “artificial neural network" OR “ANN" OR
“convolutional neural network" OR “CNN" OR “deep neural network" OR “DNN" OR
“machine learning" or “adversarial network" or “GAN").
The initial search yielded 16,209 and 2129 items, respectively, from Scopus
and Web of Science (WOS).

###  3.2 Screening

Once the search period was narrowed to 2012-2021 and only peer-reviewed
English papers were evaluated, the total number of articles decreased to 9117
for Scopus and 1803 for WOS, respectively.

###  3.3 Eligibility and Inclusion

These publications were chosen for further examination if they are open access
and are journal articles. There were 1216 full-text articles (724 from the
Scopus database and 492 from WOS). Bibliographic analysis was performed on all
1216 publications.
One investigator (Z.S.) imported the 1216 article information as excel CSV
data for future analysis. Excel duplication functions were used to identify
and eliminate duplicates. Two independent reviewers (M.A. and Z.S.) examined
the titles and abstracts of 1192 publications. Disagreements were settled
through conversation. We omitted studies that were not relevant to Machine
Learning but were relevant to disease diagnosis or vice versa.
After screening the titles and abstracts, the complete text of 102 papers was
examined, and all 102 articles satisfied all inclusion requirements. Factors
that contributed to the article’s exclusion from the full-text screening
includes:

  1. 1.

Inaccessibility of the entire text

  2. 2.

Non-human studies, book chapters, reviews

  3. 3.

Incomplete information related to test result

Fig. 4 shows the flow diagram of the systematic article selection procedure
used in this study.

Figure 4: MLBDD article selection procedure used in this study.

##  4 Bibliometric Analysis

The bibliometric study in this section was carried out using reference
literature gathered from the Scopus and WOS databases. The bibliometric study
examines publications in terms of the subject area, co-occurrence network,
year of publication, journal, citations, countries, and authors.

###  4.1 Subject Area

Many research disciplines have uncovered machine learning-based disease
diagnostics throughout the years. Figure 5 depicts a schematic representation
of machine learning-based disease detection spread across several research
fields. According to the graph, computer science (40%) and engineering (31.2%)
are two dominating fields that vigorously concentrated on MLBDD.

Figure 5: Distribution of articles by subject area.

###  4.2 Co-Occurrence Network

Co-occurrence of keywords provides an overview of how the keywords are
interconnected or used by the researchers. Fig. 6 displays the co-occurrence
network of the article’s keywords and their connection, developed by VOSviewer
software. The Figure shows that some of the significant clusters include
Neural Networks (NN), Decision Trees (DT), Machine Learning (ML), and Logistic
Regression (LR). Each cluster is also connected with other keywords that fall
under that category. For instance, the NN cluster contains Support Vector
Machine (SVM), Parkinson’s disease, and classification.

Figure 6: Bibliometric map representing co-occurrence analysis of keywords in
network visualization.

###  4.3 Publication by Year

The exponential growth of journal publications is observed from 2017. Fig. 7
displays the number of publications between 2012 to 2021 based on the Scopus
and WOS data. Note that while the image may not accurately depict the MLBDD’s
real contribution, it does illustrate the influence of MLBDD over time.

Figure 7: Publications of Machine Learning-based Disease Diagnosis (MLBDD) by
year.

###  4.4 Publication by Journal

We investigated the most prolific Journals in MLBDD domains based on our
referred literature.The top ten journals and the number of articles published
in the last ten years are depicted in Fig. 8. IEEE Access and Scientific
Reports are the most productive journals that published 171 and 133 MLBDD
articles respectively.

Figure 8: Publications by Journals

###  4.5 Publication by Citations

Citations are one of the primary indicators of an article’s effect. Here we
have identified the top ten cited articles using the R-studio tool. Table 1
summarizes the top articles that achieved the highest citation during the year
between 2012 to 2021. Note that, Google Scholar and other online databases may
have various indexing procedures and times, therefore the citations in this
manuscript may differ from the number of citations shown in this study. The
Table shows that published articles by Motwani et al. (2017) [27] earned the
most citations (257), with 51.4 citations per year, followed by Gray et al.
(2013) [28]’s article, which obtained 218 citations. It is assumed that all
the authors included in Table 1 are among those prominent authors that
contributed to MLBDD.

Table 1: Top ten cited papers published in MLBDD in between 2012-2021 based on Scopus and WOS database. Author(s) |  Article Titles |  Citation
---|---|---
Motwani et al. (2017 )[27] |  Machine Learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis |  257
Gray et al. (2013) [28] |  Random forest-based similarity measures for multi-modal classification of Alzheimer’s disease |  248
Mohan et al. (2019) [29] |  Effective Heart disease prediction Using hybrid Machine Learning techniques |  214
Yadav & Jadvav (2019) [30] |  Deep Convolutional Neural Network based medical image classification for disease diagnosis |  155
Zhang et al. (2015 )[31] |  Detection of subjects and brain regions related to Alzheimer’s disease using 3D MRI scans based on Eigenbrain and Machine Learning |  147
Austin et al. (2013) [32] |  Using methods from the data-mining and Machine-Learning literature for disease classification and prediction: a case study examining classification of Heart failure subtypes |  139
Sharmila & Geethanjali (2016 )[33] |  DWT based detection of Epileptic Seizure From EEG signals using Naive Bayes and k-NN classifiers |  134
Lebedev et al. (2014) [34] |  Random Forest ensembles for detection and prediction of Alzheimer’s disease with a good between-cohort robustness |  129
Luz et al. (2013) [35] |  ECG Arrhythmia classification based on optimum-path forest |  111
Challis et al. (2015) [36] |  Gaussian process classification of Alzheimer’s disease and mild cognitive impairment from resting-state fMRI |  107

###  4.6 Publication by Countries

Fig. 9 displayed that, China published the most publications in MLBDD, total
259 articles. USA and India are placed 2nd and 3rd, respectively as they
published 139 and 103 papers related to MLBDD. Interestingly, four out of the
top ten productive countries are from Asia: China, India, Korea, and Japan.

Figure 9: Top ten countries that contributed to MLBDD literature.

###  4.7 Publication by Author

According to Table 2 Author Kim J published the most publications (20 out of
1216). Wang Y and Li J Ranked 2nd and 3rd by publishing 19 and 18 articles
respectively. As shown in Table 2, the number of papers produced by the top 10
authors ranges between 15-20.

Table 2: Top ten authors based on total number of publications Author | Total Article
---|---
Kim J | 20
Wang Y | 19
Li J | 18
Liu Y | 18
Chen Y | 17
Kim H | 16
Kim Y | 15
Lee S | 15
Li Y | 15
Wang L | 15

##  5 Machine Learning Techniques for Different Disease Diagnosis

Many academics and practitioners have used Machine Learning (ML) approaches in
disease diagnosis. This section describes many types of Machine Learning-based
Disease Diagnosis (MLBDD) that have received much attention because of their
importance and severity. For example, due to the global relevance of COVID-19,
several studies concentrated on COVID-19 disease detection using ML from 2020
to the present, which also received greater priority in our study. Severe
diseases such as Heart disease, Kidney disease, Breast cancer, Diabetes,
Parkinson’s, Alzheimer’s, and COVID-19 are discussed briefly, while other
diseases are covered briefly under the "other disease."

###  5.1 Heart Disease

Most researchers and practitioners use Machine Learning (ML) approaches to
identify cardiac disease [37, 38]. Ansari et al. (2011), for example, offered
an automated coronary Heart disease diagnosis system based on Neuro-Fuzzy
integrated systems that yield around 89% accuracy [37]. One of the study’s
significant weaknesses is the lack of a clear explanation for how their
proposed technique would work in various scenarios such as multiclass
classification, big data analysis, and unbalanced class distribution.
Furthermore, there is no explanation about the credibility of the model’s
accuracy, which has lately been highly encouraged in medical domains,
particularly to assist users who are not from the medical domains in
understanding the approach.
Rubin et al. (2017) uses Deep Convolutional Neural Network-based approaches to
detect irregular cardiac sounds. The authors of this study adjusted the loss
function to improve the training dataset’s sensitivity and specificity. Their
suggested model was tested in the 2016 PhysioNet computing competition. They
finished second in the competition, with a final prediction of 0.95
specificity and 0.73 sensitivity [39].
Aside from that, Deep Learning (DL)-based algorithms have lately received
attention in detecting cardiac disease. Miao & Miao et al. (2018), for
example, offered a DL-based technique to diagnosing cardiotocographic fetal
health based on a multiclass morphologic pattern. The created model is used to
differentiate and categorize the morphologic pattern of individuals suffering
from pregnancy complications. Their preliminary computational findings include
accuracy of 88.02%, a precision of 85.01%, and an F-score of 0.85 [40]. During
that study, they employed multiple dropout strategies to address overfitting
problems, which finally increased training time, which they acknowledged as a
tradeoff for higher accuracy.
Although ML applications have been widely employed in Heart disease diagnosis,
no research has been conducted that addressed the issues associated with
unbalanced data with multiclass classification. Furthermore, the model’s
explainability during final prediction is lacking in most cases. Table 3
summarizes some of the cited publications that employed ML and DL approaches
in the diagnosis of cardiac disease. However, further information about
Machine Learning-based cardiac disease diagnosis can be found in [5].

Table 3: Referenced literature that considered Machine Learning based Heart disease diagnosis. Authors |  Contributions |  Algorithm |  Dataset |  Data type |  Performance evaluation
---|---|---|---|---|---
Bemando et al. (2021 )[41] |  Predict coronary heart disease |  Gaussian NB, Bernoulli NB and RF |  Cleveland dataset |  Tabular |  Accuracy- 85.00%, 85.00% and 75.00%
Kumar & Polepaka (2020) [42] |  Predicting heart diseases |  RF, CNN |  Cleveland dataset |  Tabular |  RF (Accuracy- 80.327%, Precision- 82%, Recall- 80%, F1-score- 80%), CNN (Accuracy- 78.688, Precision- 80%, Recall- 79%, F1-score- 78%)
Sing et al. (2019) [43] |  Heart disease classification |  SVM |  Cleveland database |  Tabular |  Accuracy- 73%-91%
Desai et al. (2019) [44] |  Heart disease classification |  Back-propagation NN, LR |  Cleveland dataset |  Tabular |  Accuracy (BNN- 85.074%, LR- 92.58%)
Patil et al. (2018) [45] |  ECG arrhythmia for heart disease detection |  SVM and Cuckoo search optimized NN |  Cleveland dataset |  Tabular |  Accuracy (SVM-94.44%)
Liu et al. (2012) [46] |  Intelligent scoring system for the prediction of cardiac arrest within 72h |  SVM |  Privately ownend |  Tabular |  Specificity- 78.8%, Sensitivity- 62.3%, Positive predictive value- 10%, Negative predictive value- 98.2%
Acharya et al. (2017) [47] |  Automatically identify 5 different categories of heartbeats in ECG signals |  CNN |  MIT-BIH |  Tabular |  Accuracy- 94% (balance data) Accuracy- 89.07% (imbalance data)
Yang et al. (2018) [48] |  Novel heartbeat recognition method is presented |  SVM |  MIT-BIH |  Tabular |  Accuracy- 97.77% (imbalance data), Accuracy- 97.08%( noise-free ECGs)

###  5.2 Kidney Disease

Kidney disease, often known as Renal disease, refers to nephropathy or kidney
damage. Patients with Kidney disease have decreased kidney functional
activity, which can lead to kidney failure if not treated promptly. According
to the National Kidney Foundation, 10% of the world’s population has chronic
kidney disease (CKD), and millions die each year due to insufficient
treatment.. The recent advancement of ML and DL-based kidney disease diagnosis
may provide a possibility for those countries that are unable to handle the
kidney disease diagnostic related tests [49]. For instance, Charleonnan et al.
(2016) used publicly available datasets to evaluate four different ML
algorithms: K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Logistic
Regression (LR), and Decision Tree classifiers and received the accuracy of
98.1%, 98.3%, 96.55%, and 94.8%, respectively [50]. Aljaaf et al. (2018)
conducted a similar study. The authors tested different ML algorithms,
including RPART, SVM, LOGR, and MLP, using a comparable dataset, CKD, as used
by [50], and found that MLP performed best (98.1 percent) in identifying
chronic kidney disease [51]. To identify chronic kidney disease, Ma et al.
(2020) utilizes a collection of datasets containing data from many sources
[52]. Their suggested Heterogeneous Modified Artificial Neural Network (HMANN)
model obtained an accuracy of 87%-99%.

Table 4 summarizes some of the cited publications that employed ML and DL
approaches to diagnose kidney disease.

Table 4: Referenced literature that considered machine learning based kidney disease diagnosis. Authors |  Contributions |  Algorithm |  Dataset |  Data type |  Performance evaluation
---|---|---|---|---|---
Wasle et al. (2020) [13] |  Analysis of Chronic Kidney Disease |  NB, DT, and RF |  Chronic kidney disease dataset |  Tabular |  Accuracy- 100% (RF)
Nithya et al. (2020) [53] |  Kidney disease detection and segmentation |  ANN and multi-kernel k-means clustering |  100 collected image data of patients Ultrasound |  Image |  Accuracy- 99.61%
Al Imran et al. (2018) [54] |  Classification of Chronic kidney disease |  LR, Feedforward NN and Wide DL |  Chronic kidney disease dataset |  Tabular |  Feedforward NN (F1-score- 99%, Precision- 97%, Recall- 99%, and AUC-99%)
Navaneeth & Suchetha (2020 [55] |  Chronic kidney disease |  CNN-SVM |  Privately own dataset |  Tabular |  Accuracy-97.67%, Sensitivity– 97.5%, Specificity- 97.83%
Brunetti et al. (2019) [56] |  Detection and localization of kidneys in patients with autosomal dominant polycystic |  CNN |  Privately own data |  Image |  Accuracy- 95%

###  5.3 Breast Cancer

Many scholars in the medical field have proposed Machine Learning (ML)-based
Breast cancer analysis as a potential solution to early-stage diagnosis.
Miranda & Felipe (2015), for example, proposed Fuzzy Logic-based computer-
aided diagnosis systems for breast cancer categorization. The advantage of
Fuzzy Logic over other classic ML techniques is that it can minimize
computational complexity while simulating the expert radiologist’s reasoning
and style. If the user inputs parameters such as contour, form, and density,
the algorithm offers a cancer categorization based on their preferred method
[57]. Miranda & Felipe (2015)’s proposed model had an accuracy of roughly
83.34%. The authors employed an approximately equal ratio of images for the
experiment, which resulted in improved accuracy and unbiased performance.
However, as the study did not examine the interpretation of their results in
an explainable manner, it may be difficult to conclude that accuracy, in
general, indicates true accuracy for both benign and malignant
classifications. Furthermore, no confusion matrix is presented to demonstrate
the models’ actual prediction for the each class.
Zheng et al. (2014) presented hybrid strategies for diagnosing Breast cancer
disease utilizing k-Means Clustering (KMC) and SVM. Their proposed model
considerably decreased the dimensional difficulties and attained an accuracy
of 97.38% using Wisconsin Diagnostic Breast Cancer (WDBC) dataset [58]. The
dataset is normally distributed and has 32 features divided into 10
categories. It is difficult to conclude that their suggested model will
outperform in a dataset with an unequal class ratio, which may contain missing
value as well.
To determine the best ML models, Asri et al. (2016) applied various ML
approaches such as SVM, DT (C4.5), NB, and KNN on the Wisconsin Breast Cancer
(WBC) datasets. According to their findings, SVM outperformed all other ML
algorithms, obtaining an accuracy of 97.13% [59]. However, if a same
experiment is repeated in a different database, the results may differ.
Furthermore, experimental results accompanied by ground truth values may
provide a more precise estimate in determining which ML model is the best or
not.
Mohammed et al. (2020) conducted a nearly identical study. The authors employ
three ML algorithms to find the best ML methods: DT (J48), NB, and Sequential
Minimal Optimization (SMO), and the experiment was conducted on two popular
datasets: WBC and Breast Cancer dataset. One of the interesting aspects of
this research is that they focused on data imbalance issues and minimized the
imbalance problem through the use of resampling data labeling procedures.
Their findings showed that the SMO algorithms exceeded the other two
classifiers, attaining more than 95% accuracy on both datasets [60]. However,
in order to reduce the imbalance ratio, they used resampling procedures
numerous times, potentially lowering the possibility of data diversity. As a
result, the performance of those three ML methods may suffer on a dataset that
is not normally distributed or imbalanced.
Assegie (2021) used the grid search approach to identify the best K-Nearest
Neighbor (KNN) settings. Their investigation showed that parameter adjustment
had a considerable impact on the model’s performance. They demonstrated that
by fine-tuning the settings, it is feasible to get 94.35% accuracy, whereas
the default KNN achieved around 90% accuracy [61].
To detect breast cancer, Bhattacherjee et al. (2020) employed a Back-
propagation Neural Network (BNN). The experiment was carried out in the WBC
dataset with 9 features, and they achieved 99.27% accuracy [62]. Alshayeji et
al. (2021) used the WBCD and WDBI datasets to develop a shallow- ANN model for
classifying Breast cancer tumors. The authors demonstrated that the suggested
model could classify tumors up to 99.85% properly without selecting
characteristics or tweaking the algorithms [63].
Sultana et al. (2021) detect Breast cancer using a different ANN architecture
on the WBC dataset. They employed a variety of NN architectures, including the
Multilayer Perceptron (MLP) Neural Network, the Jordan/Elman NN, the Modular
Neural Network (MNN), the Generalized Feed-Forward Neural Network (GFFNN), the
Self-Organizing Feature Map (SOFM), the SVM Neural Network, the Probabilistic
Neural Network (PNN), and the Recurrent Neural Network (RNN). Their final
computational result demonstrates that the PNN with 98.24% accuracy
outperforms the other NN models utilized in that study [64]. However, this
study lacks the interpretability as of many other investigations because it
does not indicate which features are most important during the prediction
phase.
Deep Learning (DL) was also used by Ghosh et al. (2021). The WBC dataset was
used by the authors to train seven deep learning (DL) models: ANN, CNN, GRU,
LSTM, MLP, PNN, and RNN. Long short-term memory (LSTM) and Gated recurrent
unit (GRU) demonstrated the best performance among all DL models, achieving an
accuracy of roughly 99% [65]. Table 5 summarizes some of the referenced
literature that used ML and DL techniques in Breast cancer diagnosis.

Table 5: Referenced literature that considered Machine Learning based Breast cancer disease diagnosis. Authors |  Contributions |  Algorithm |  Dataset |  Data type |  Performance evaluation
---|---|---|---|---|---
Rajendran et al. (2020) [14] |  Breast cancer |  NB, BN, RF and DT (C4.5) |  BCSC |  Image |  ROC - 0.937 (BN)
Abdel et al. (2015) [66] |  Classification of breast density and mass |  SVM |  Mini-MIAS, INBreast |  Image |  Mini-MIAS: - Accuracy- 99%, AUC- 0.9325
Sharma & Khanna (2015) [67] |  Classify vector features as malignant or non-malignant |  SVM |  IRMA ,DDSM |  Image |  IRMA: Sensitivity- 99%, Specificity- 99% , DDSM: Sensitivity- 97%, Specificity- 96%
Moon et al. (2017) [68] |  Classification of breast cancers by tumor size |  LR - ANN |  156 Privately owned cases |  Image |  Accuracy- 81.8%, Sensitivity- 85.4%, Specificity- 77.8%, AUC- 0.855
Lo et al. (2016) [69] |  CAD tumor |  Binary-LR |  18 Privately owned cases |  Image |  Accuracy- 80.39%
Venkatesh et al. (2015) [70] |  Differentiating malignant and benign masses |  NB, LR - AdaBoost |  246 Privately owned image |  Image |  Sensitivity- 90%, Specificity- 97.5%, AUC- 0.98

###  5.4 Diabetes

According to the International Diabetes Federation (IDF), there are currently
over 382 million individuals worldwide who have diabetes, with that number
anticipated to increase to 629 million by 2045 [71]. Numerous studies widely
presented ML-based systems for Diabetes patient detection. For
example,kandhasamy & Balamurali (2015) compared ML classifiers (J48 DT, KNN,
RF, and SVM) for classifying patients with diabetes mellitus. The experiment
was conducted on the UCI Diabetes dataset, and the KNN (K=1) and RF
classifiers obtained near-perfect accuracy [72]. However, one disadvantage of
this work is that it used a simplified Diabetes dataset with only eight
binary-classified parameters. As a result, getting 100% accuracy with a less
difficult dataset is unsurprising. Furthermore, there is no discussion of how
the algorithms influence the final prediction or how the result should be
viewed from a nontechnical position in the experiment.
Yahyaoui et al. (2019) presented a Clinical Decision Support Systems (CDSS) to
aid physicians or practitioners with Diabetes diagnosis. To reach this goal,
the study utilized a variety of ML techniques, including SVM, RF, and Deep
Convolutional Neural Network (CNN). RF outperformed all other algorithms in
their computations, obtaining an accuracy of 83.67%, while DL and SVM scored
76.81% and 65.38% accuracy, respectively [73].
Naz & Ahuja (2020) employed a variety of ML techniques, including Artificial
neural networks (ANN), NB, DT, and DL, to analyze open-source PIMA Diabetes
datasets. Their study indicates that DL is the most accurate method for
detecting the development of Diabetes, with an accuracy of approximately
98.07% [71]. The PIMA dataset is one of the most thoroughly investigated and
primary datasets, making it easy to perform conventional and sophisticated ML-
based algorithms. As a result, gaining greater accuracy with the PIMA Indian
dataset is not surprising. Furthermore, the paper makes no mention of
interpretability issues and how the model would perform with an unbalanced
dataset or one with a significant number of missing variables. As is widely
recognized in healthcare, several types of data can be created that are not
always labeled, categorized, and preprocessed in the same way as the PIMA
Indian dataset. As a result, it is critical to examine the algorithms’
fairness, unbiasedness, dependability, and interpretability while developing a
CDSS, especially when a considerable amount of information is missing in a
multiclass classification dataset.
Ashiquzzaman et al. (2017) developed a Deep Learning strategy to address the
issue of overfitting in Diabetes datasets. The experiment was carried out on
the PIMA Indian dataset and yielded an accuracy of 88.41%. The authors claimed
that performance improved significantly when dropout techniques were utilized
and the overfitting problems were reduced [74]. Overuse of the dropout
approach, on the other hand, lengthens overall training duration. As a result,
as they did not address these concerns in their study, assessing whether their
proposed model is optimum in terms of computational time is difficult.
Alhassan et al. (2018) introduced the King Abdullah International Research
Center for Diabetes (KAIMRCD) dataset, which includes data from 14k people and
is the world’s largest Diabetic dataset. During that experiment, the author
presented a CDSS architecture based on LSTM and GRU-based Deep Neural
Networks, which obtained up to 97% accuracy [75]. Table 6 highlights some of
the relevant publications that employed ML and DL approaches in the diagnosis
of Diabetic disease.

Table 6: Referenced literature that considered Machine Learning based Diabetic disease diagnosis. Authors |  Contributions |  Algorithm |  Dataset |  Data type |  Performance evaluation
---|---|---|---|---|---
Fitriyani et al. (2019) [76] |  Diabetes and hypertension |  DPM |  Privately owned |  Tabular |  Accuracy- 96.74%
Fernandez et al. (2021) [77] |  Type 1 diabetes |  RF |  DIABIM-MUNE |  Tabular |  AUC- 0.80
Ali et al. (2020) [78] |  Diabetes classification |  KNN |  Privately owned- 4900 samples |  Tabular |  Accuracy- 99.9%
Tsao et al. (2018) [15] |  Predict diabetic retinopathy and identify interpretable biomedical features |  SVM, DT, ANN, and LR |  Privately owned |  Tabular |  SVM (Accuracy- 79.5%, AUC- 0.839)
Qtea et al. (2021) [79] |  Diabetes classification |  PSO and MLPNN |  Privately owned |  Tabular |  Accuracy- 98.73%

###  5.5 Parkinson’s Disease

Parkinson’s disease is one of the conditions that has received a lot of
attention in the ML literature. It is a slow-progressing chronic neurological
disorder. When dopamine-producing neurons in certain parts of the brain are
harmed or die, people have difficulty speaking, writing, walking, and doing
other core activities [80]. There are several ML-based approaches have been
proposed. For instance, Sriram et al. (2013) used KNN, SVM, NB, and RF
algorithms to develop intelligent Parkinson’s disease diagnosis systems. Their
computational result shows that, among all other algorithms, RF shows the best
performance (90.26% accuracy), and NB demonstrate the worst performance
(69.23% accuracy) [81].
Esmaeilzadeh et al. (2018) proposed a Deep CNN-based model to diagnose
Parkinson’s disease and achieved almost 100% accuracy on train and test set
[82]. However, there was no mention of any overfitting difficulties in the
trial. Furthermore, the experimental results do not provide a good
interpretation of the final classification and regression, which is now widely
expected, particularly in CDSS. Grover et al. (2018) also used DL-based
approaches on UCI’s Parkinson’s telemonitoring voice dataset. Their experiment
using DNN has achieved around 81.67% accuracy in diagnosing patients with
Parkinson’s disease symptoms [80].
Warjurkar & Ridhorkar (2021) conducted a thorough study on the performance of
the ML-based approach in decision support systems that can detect both Brain
tumors and diagnose Parkinson’s patients. Based on their findings, it was
obvious that, when compared to other algorithms, Boosted Logistic Regression
surpassed all other models, attaining 97.15% accuracy in identifying
Parkinson’s disease patients. In tumor segmentation, however, the Markov
random technique performed best, obtaining an accuracy of 97.4% [83].
Parkinson’s disease diagnosis using ML and DL approaches is summarized in
Table 7 , which includes a number of references to the relevant research.

Table 7: Referenced literature that considered Machine Learning based parkinson disease diagnosis. Authors |  Contributions |  Algorithm |  Dataset |  Data type |  Performance evaluation
---|---|---|---|---|---
Sherly et al. (2021) [84] |  Parkinson’s disease |  KMC and DT |  Privately owned |  Speech |  Accuracy- 95.56%
Nurrohman et al. (2021) [16] |  Parkinson’s disease subtype classification |  DT, LR |  PPMI |  Tabular |  Accuracy- 98.3%, Sensitivity- 98.41%, and Specificity- 99.07%
Asmae et al. (2020)[85] |  Parkinson’s disease identification |  KNN and ANN |  Parkinson’s UI machine learning dataset |  Tabular |  ANN (Accuracy- 96.7%)
Guruler et al. (2017) [86] |  Diagnosis system for Parkinson’s disease |  ANN, KMC |  Parkinsons dataset |  Speech and sound |  Accuracy- 99.52%
Shetty et al. (2016) [87] |  identify Parkinson’s disease |  SVM |  NIHS |  Speech and sound |  Accuracy- 83.33%, True positive- 75%, False positive- 16.67%

###  5.6 COVID-19

The new Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), also
known as COVID-19, pandemic has become humanity’s greatest challenge in
contemporary history. Despite the fact that a vaccine had been advanced in
distribution because to the global emergency, it was unavailable to the
majority of people for the duration of the crisis [88]. Because of the new
COVID-19 Omicron strain’s high transmission rates and vaccine-related
resistance, there is an extra layer of concern. The gold standard for
diagnosing COVID-19 infection is now Real-Time Reverse Transcription-
Polymerase Chain Reaction (RT-PCR) [89, 90]. Throughout the epidemic, the
researcher advocated other technologies including as chest X-rays and Computed
Tomography (CT) combined with Machine Learning and Artificial Intelligence to
aid in the early detection of people who might be infected. For example, Chen
et al. (2020) proposed a UNet++ model employing CT images from 51 COVID-19 and
82 non-COVID-19 patients and achieved an accuracy of 98.5% [91]. Ardakani et
al. (2020) used a small dataset of 108 COVID-19 and 86 non-COVID-19 patients
to evaluate ten different DL models and achieved a 99% overall accuracy [92].
Wang et al. (2020) built an inception-based model with a large dataset,
containing 453 CT scan images, and achieved 73.1% accuracy. However, the
model’s network activity and region of interest were poorly explained [93] .
Li et al. (2020) suggested the COVNet model and obtain 96% accuracy utilizing
a large dataset of 4356 chest CT images of Pneumonia patients, 1296 of which
were verified COVID-19 cases [94].
Several studies investigated and advised screening COVID-19 patients utilizing
chest X-ray images in parallel, with major contributions in [95, 96, 97]. For
example,Hemdan et al. (2020) used a small dataset of only 50 images to
identify COVID-19 patients from chest X-ray images with an accuracy of 90% and
95%, respectively, using VGG19 and ResNet50 models [95]. Using a dataset of
100 chest X-ray images, Narin et al. (2021) distinguished COVID-19 patients
from those with Pneumonia with 86% accuracy [97].
In addition, in order to develop more robust and better screening systems,
other studies considered larger datasets. For example, Brunese et al. (2020)
employed 6505 images with a data ratio of 1:1.17, with 3003 images classified
as COVID-19 symptoms and 3520 as "other patients" for the objectives of that
study [98]. With a dataset of 5941 images, Ghoshal & Tucker (2020) achieved
92.9% accuracy [99]. However, neither study looked at how their proposed
models would work with data that was severely unbalanced and had mismatched
class ratios. Apostolopoulos & Mpesiana (2020) employed a CNN-based Xception
model on an imbalanced dataset of 284 COVID-19 and 967 non-COVID-19 patient
chest X-ray images and achieved 89.6% accuracy [100].
The following Table 8 summarizes some of the relevant literature that employed
ML and DL approaches to diagnose COVID-19 disease.

Table 8: Referenced literature that considered Machine Learning based COVID-19 disease diagnosis. Authors |  Contributions |  Algorithm |  Dataset |  Data type |  Performance evaluation
---|---|---|---|---|---
Li et al. (2020) [94] |  COVID-19 disease detection |  CNN |  Mixed dataset |  Image |  Accuracy- 90%
Chen et al. (2020) [91] |  COVID-19 disease detection |  CNN |  Mixed dataset |  Image |  Accuracy- 98.5%
Song et al. (2021) [101] |  COVID-19 disease detection |  CNN |  Mixed dataset |  Image |  Accuracy- 86%
Jin et al. (2021) [102] |  COVID-19 disease detection |  CNN |  Cohen’s dataset |  Image |  Accuracy- 94.1%
Ahsan et al. (2020) [6] |  COVID-19 disease detection and image segmentation |  CNN |  Cohen’s dataset |  Image and Tabular |  Accuracy- 95.38%

###  5.7 Alzheimer’s Disease

Alzheimer is a brain illness that often begins slowly but progresses over
time, and it affects 60%-70% of those who are diagnosed with dementia [103].
Alzheimer’s disease symptoms include language problems, confusion, mood
changes, and other behavioral disorders. Body functions gradually
deteriorated, and the usual life expectancy is three to nine years after
diagnosis. Early diagnosis, on the other hand, may assist to avoid and take
required actions to enter into suitable treatment as soon as possible, which
will also raise the possibility of life expectancy. Machine Learning and Deep
Learning have shown promising outcomes in detecting Alzheimer’s disease
patients throughout the years. For instance,Neelaveni & Devasana (2020)
proposed a model that can detect Alzheimer patients using SVM and DT, and
achieved an accuracy of 85% and 83% respectively [104]. Collij et al. (2016)
also used SVM to detect single-subject Alzheimer’s disease and Mild Cognitive
Impairment (MCI) prediction and achieved an accuracy of 82% [105].
Multiple algorithms have been adopted and tested in developing ML based
Alzheimer disease diagnosis. For example, Vidushi & Shrivastava (2019)
experimented using Logistic Regression (LR), SVM, DT, ensemble Random Forest
(RF), and Boosting Adaboost and achieved an accuracy of 78.95%, 81.58%,
81.58%, 84.21%, and 84.21% respectively [106]. Many of the study adopted CNN
based approach to detect Alzheimer patients as CNN demonstrates robust results
in image processing compared to other existing algorithms. As a consequence,
Ahmed et al. (2020) proposed a CNN model for earlier diagnosis and
classification of Alzheimer disease. Within the dataset consists of 6628 MRI
images, the proposed model achieved 99% accuracy [107]. Nawaz et al. (2020)
proposed deep feature-based models and achieved an accuracy of 99.12% [108].
Additionally, Studies conducted by Haft-Javaherian et al. (2019) [109] and
Aderghal et al. (2017) [110] are some of the CNN based study that also
demonstrates the robustness of CNN based approach in Alzheimer disease
diagnosis. ML and DL approaches employed in the diagnosis of Alzheimer’s
disease are summarized in Table 9.

Table 9: Referenced literature that considered Machine Learning based Alzheimer disease diagnosis. Authors |  Contributions |  Algorithm |  Dataset |  Data type |  Performance evaluation
---|---|---|---|---|---
Sun et al. (2021) [111] |  Automatic diagnosis of alzheimer’s disease and mild cognitive impairment |  CNN+SVM |  F-FDG PET :PET |  Image |  Accuracy- 74%-90%
Kunag et al. (2021) [112] |  Predicting transition from mild cognitive impairment to alzheimer’s |  LR,ARN,DT |  1913 privately owned cases |  Tabular |  Accuracy- (89.52 ± 0.36%), AUC-ROC (92.08 ± 0.12), Sensitivity- (82.11 ± 0.42%) and Postivie predictive value (75.26 ± 0.86%)
Manzak et al. (2019) [113] |  Automatic classification of alzheimer’s |  DNN+ RF |  Tabular |  Accuracy- 67% |

###  5.8 Other Diseases

Beyond the disease mentioned above, ML and DL have been used to identify
various other diseases. Big data and increasing computer processing power are
two key reasons for this increased use. For example, Mao et al. (2020) used
Decision Tree (DT) and Random Forest (RF) to disease classification based on
eye movement [114]. Nosseir & Shawky (2019) evaluated KNN and SVM to develop
automatic skin disease classification systems, and the best performance was
observed using KNN by achieving an accuracy of 98.22% [115]. Khan et al.
(2020) employed CNN-based approaches such as VGG16 and VGG19 to classify
multimodal Brain tumors. The experiment was carried out using publicly
available three image datasets: BraTs2015, BraTs2017, and BraTs2018, and
achieved 97.8%, 96.9%, and 92.5% accuracy, respectively [116]. Amin et al.
(2018) conducted a similar experiment utilizing the RF classifier for tumor
segmentation. The authors achieved 98.7%, 98.7%, 98.4%, 90.2%, and 90.2%
accuracy using BRATS 2012, BRATS 2013, BRATS 2014, BRATS 2015, and ISLES 2015
dataset, respectively [117].
Dai et al. (2019) proposed a CNN-based model to develop an application to
detect Skin cancer. The authors used a publicly available dataset, HAM10000,
to experiment and achieved 75.2% accuracy [118] . Daghrir et al. (2020)
evaluated KNN, SVM, CNN, Majority Voting using ISIC (International Skin
Imaging Collaboration) dataset to detect Melanoma skin cancer. The best result
was found using Majority Voting (88.4% accuracy) [119]. Table 10 summarizes
some of the referenced literature that used ML and DL techniques in various
disease diagnosis.

Table 10: Referenced literature that considered Machine Learning on various disease diagnosis. Authors |  Contributions |  Algorithm |  Dataset |  Data type |  Performance evaluation
---|---|---|---|---|---
Dhaliwal et al. (2021) [120] |  Classify pediatric colonic inflammatory bowel disease subtype |  RF |  74 Privately owned cases |  Image |  Accuracy- 100%
Fathi et al. (2020) [121] |  classification of liver diseases |  svm |  ILPD and BUPA |  Tabular |  Accuracy- 90%-92%, Sensitivity- 89%-91%, F1-score- 94%-94.3%
Wang et al. (2014) [122] |  Hypertension |  LR and ANN |  BRFSS |  Tabular |  Accuracy- 72%, AUC> 0.77
Kalaiselvi et al. (2020) [123] |  Brain tumor diagnostic |  CNN |  Brain tumor challenge websites and MRI centers |  Image |  Accuracy- 90%-99%
Usman & Rajpoot (2017) [124] |  Brain tumor segmentation for multi-modality MRI |  RF |  MICCAI, BraTS 2013 |  Image |  88% disc overlap
Waheed et al. (2017) [125] |  Melanoma detection with dermoscopic images |  SVM with color and feature extractor |  PH2 |  Image |  Accuracy- 96%
Kamboj et al. (2018) [126] |  Melanoma skin cancer detection |  NB, DT, and KNN |  MED-NODE |  Image |  DT (Accuracy- 82.35%)
Magalhaes et al. (2021) [127] |  Skin cancer detection with infrared thermal imaging |  Ensemble learning and DL |  |  Image |  Precision- 0.9665, Recall- 0.9411, F1-score- 0.9536, ROC-AUC- 0.9185
Chen et al. (2020) [128] |  Hepatocellular carcinoma |  InceptionV3 |  Genomic data commons databases |  Image |  Accuracy- 89%-96%
Das et al. (2019) [129] |  Identification of liver cancer |  Watershed gaussian based DL (WGDL) |  Privately owned |  Image |  Accuracy- 99.38%
Wang et al. (2021) [130] |  Hepatocellular carcinoma (HCC) postoperative death outcomes |  RF,Gradient boosting,Gbm, LR,DT |  BioStudies database |  Tabular |  AUC- 0.803 (RF)

##  6 Algorithm and Dataset Analysis

Most of the referenced literature considered multiple algorithms in MLBDD
approaches. Here we have addressed multiple algorithms as hybrid approaches.
For instance, Sun et al. (2021) used hybrid approaches to predict coronary
Heart disease using Gaussian Naïve Bayes, Bernoulli Naïve Bayes, and Random
Forest (RF) algorithms [111]. Bemando et al. (2021) adopted CNN and SVM to
automate the diagnosis of Alzheimer’s disease and mild cognitive impairment
[41]. Saxena et al. (2019) used KNN and Decision Tree (DT) in Heart disease
diagnosis [131]; Elsalamony (2018) employed Neural Networks (NN) and SVM in
detecting Anaemia disease in human red blood cells [132]. One of the key
benefits of using the hybrid technique is that it is more accurate than using
single ML models.
According to the relevant literature, the most extensively utilized induvial
algorithms in developing MLBDD models are CNN, SVM, and LR. For instance,
Kalaiselvi et al. (2020) proposed CNN based approach in Brain tumor diagnosis
[123]; Dai et al. (2019) used CNN in developing a device inference app for
Skin cancer detection [118];Fathi et al. (2020) used SVM to classify liver
diseases [121]; Sing et al. (2019) used SVM to classify the patients with
Heart disease symptoms [43];and Basheer et al. (2019) used Logistic Regression
to detect Heart disease [133].
Fig. 10 depicts the most commonly used Machine Learning algorithms in disease
diagnosis. The bolder and larger font emphasizes the importance and frequency
with which the algorithms in MLBDD are used. Based on the Figure, we can
observe that Neural Networks, CNN, SVM, and Logistic Regression are the most
commonly employed algorithms by MLBDD researchers.

Figure 10: Word cloud for most frequently used ML algorithms in MLBDD
publications.

Most MLBDD researchers utilize publically accessible datasets since they do
not require permission and provide sufficient information to do the entire
study. Manually gathering data from patients, on the other hand, is time-
consuming; yet, numerous research utilized privately collected/owned data,
either owing to their special necessity based on their experiment setup or to
produce a result with actual data [46, 55, 56, 68, 70]. The Cleveland Heart
disease dataset, PIMA dataset, and Parkinson dataset are the most often
utilized datasets in disease diagnosis areas. Table 11 lists publicly
available datasets and sources that may be useful to future academics and
practitioners.

Table 11: Most widely used disease diagnosis dataset URL along with the referenced literature. Study |  Disease |  Dataset |  URL
---|---|---|---
[41, 134, 135, 42, 43, 44, 45] |  Heart disease |  Cleveland database |  https://archive.ics.uci.edu/ml/datasets/heart+disease
[13, 54, 50, 51] |  Kidney disease |  Chronic kidney disease dataset |  https://archive.ics.uci.edu/ml/datasets/Chronic_Kidney_Disease
[136, 72, 71, 74] |  Diabetics |  Pima diabetic dataset |  https://www.kaggle.com/uciml/pima-indians-diabetes-database
[86, 85, 16, 81] |  Parkinson disease |  Parkinsons Dataset |  https://archive.ics.uci.edu/ml/datasets/parkinsons
[58, 59, 60] |  Breast cancer |  WDBC dataset |  https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)
[2, 97] |  COVID-19 |  Covid-chest X-ray dataset |  https://github.com/ieee8023/covid-chestxray-dataset

##  7 Discussion

In the last 10 years, Machine Learning (ML) and Deep Learning (DL) have grown
in prominence in disease diagnosis, which the annotated literature has
strengthened in this study. The review began with specific research questions
and attempted to answer them using the reference literature. According to the
overall research, CNN is one of the most emerging algorithms, outperforming
all other ML algorithms due to its solid performance with both image and
tabular data [94, 128, 137, 123]. Transfer learning is also gaining popularity
since it does not necessitate constructing a CNN model from scratch and
produces better results than typical ML methods [91, 47]. Aside from CNN, the
reference literature lists SVM, RF, and DT as some of the most common
algorithms utilized widely in MLBDD. Furthermore, several researchers are
emphasizing ensemble techniques in MLBDD [127, 130]. Nonetheless, when
compared to other ML algorithms, CNN is the most dominating. VGG16, VGG19,
ResNet50, and UNet++ are among of the most prominent CNN architectures
utilized widely in disease diagnosis.
In terms of databases, it was discovered that UCI repository data is the
preferred option of academics and practitioners for constructing a Machine
Learning-based Disease Diagnosis (MLBDD) model. However, while the current
dataset frequently has shortcomings, several researchers have recently relied
on additional data acquired from the hospital or clinic (i.e., imbalance data,
missing data). To assist future researchers and practitioners interested in
studying MLBDD, we have included a list of some of the most common datasets
utilized in the reference literature in Table 11, along with the link to the
repository.
As previously indicated, there were several inconsistencies in terms of
assessment measures published by the literature. For example, some research
reported their results with accuracy [45]; others provided with accuracy,
precision, recall, and F1-score [42]; while a few studies emphasized
sensitivity, specificity, and true positive [67]. As a result, there were no
criteria for the authors to follow in order to report their findings correctly
and genuinely. Nonetheless, of all assessment criteria, accuracy is the most
extensively utilized and recognized by academics.
With the emergence of COVID-19, MLBDD research switched mostly on Pneumonia
and COVID-19 patient detection beginning in 2020, and COVID-19 remains a
popular subject as the globe continues to battle this disease. As a result, it
is projected that the application of ML and DL in the medical sphere for
disease diagnosis would expand significantly in this domain in the future as
well. Many questions have been raised due to the progress of ML and DL-based
disease diagnosis. For example, if a doctor or other health practitioner
incorrectly diagnoses a patient, he or she will be held accountable. However,
if the machine does, who will be held accountable? Furthermore, fairness is an
issue in ML because most ML models are skewed towards the majority class. As a
result, future research should concentrate on ML ethics and fairness.
Model interpretation is absent in nearly all investigations, which is
surprising. Interpreting machine learning models used to be difficult, but
explainable and interpretable XAI have made it much easier. Despite the fact
that the previous MLBDD lacked sufficient interpretations, it is projected
that future researchers and practitioners would devote more attention to
interpreting the machine learning model due to the growing demand for model
interpretability.
The idea that ML alone will enough to construct an MLBDD model is a flawed
one. To make the MLBDD model more dynamic, it may be anticipated that the
model will need to be developed and stored on a cloud system, as the heath
care industry generates a lot of data that is typically kept in cloud systems.
As a result, the adversarial attack will focus on patients’ data, which is
very sensitive. For future ML-based models, the data bridge and security
challenges must be taken into consideration.
It is a major issue to analyze data if there is a large disparity in the data.
As the ML-based diagnostic model deals with human life, every misdiagnosis is
a possible danger to one’s health. However, despite the fact that many study
used the imbalance dataset to perform their experiment, none of the cited
literature highlights issues related to imbalance data. Thus, future work
should demonstrate the validity of any ML models while developing with
imbalanced data.
Within the many scopes this review paper also have some limitations which can
be summarized as follows:

  1. 1.

The study first searched the Scopus and WOS databases for relevant papers and
then examined other papers that were pertinent to this investigation. If other
databases like Google Scholar and Pubmed were used, the findings might be
somewhat different. As a result, our study may provide some insight into
MLBDD, but there is still a great deal of information that is outside of our
control.

  2. 2.

ML algorithms, DL algorithms, dataset, disease classifications, and evaluation
metrics are highlighted in the review. Though the suggested ML process is
thoroughly examined in reference literature, this paper does not go into that
level of detail.

  3. 3.

Only those publications that adhered to a systematic literature review
technique were included in the study’s paper selection process. Using a more
comprehensive range of keywords, on the other hand, might lead to higher
search activity. However, our SLR approach will provide researchers and
practitioners with a more thorough understanding of MLBDD.

##  8 Conclusions

This study reviewed the papers published between 2012-2021 that focused on
Machine Learning-based Disease Diagnosis (MLBDD). Researchers are particularly
interested in some diseases, such as Heart disease, Breast cancer, Kidney
disease, Diabetes, Alzheimer’s, and Parkinson’s diseases, which are discussed
considering machine learning/deep learning-based techniques. Additionally,
some other ML-based disease diagnosis approaches are discussed as well. Prior
to that, A bibliometric analysis was performed, taking into account a variety
of parameters such as subject area, publication year, journal, country, and
identifying the most prominent contributors in the MLBDD field. Because of its
remarkable performance in constructing a robust model, our major conclusion
implies that the Convolution Neural Network (CNN) is the most popular method
for researchers. We anticipate that our review will guide both novice and
expert research and practitioners in MLBDD. It would be interesting to see
some research work based on the limitations addressed in the discussion
section. Additionally, future works in MLBDD might focus on multiclass
classification with highly imbalanced data along with highly missing data,
explanation and Interpretation of multiclass data classification using XAI,
and optimizing the big data containing numerical, categorical, and image data.

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