# Predicting Patient COVID-19 Disease Severity by means of Statistical and
Machine Learning Analysis of Blood Cell Transcriptome Data

Sakifa Aktar 1, B.Sc.; Md. Martuza Ahamad 1, M.Sc.; Md. Rashed-Al-Mahfuz 2,
M.Sc.; AKM Azad 3, PhD; Shahadat Uddin 4, PhD; A H M Kamal 5, Prof.; Salem A.
Alyami 6, PhD; Ping-I Lin 7, PhD; Sheikh Mohammed Shariful Islam 8, PhD;
Julian M.W. Quinn 9, PhD; Valsamma Eapen 7,*, Prof.; Mohammad Ali Moni
7,9,10,*, PhD

1Department of Computer Science and Engineering, Bangabandhu Sheikh Mujibur
Rahman Science & Technology University, Gopalgamj, 8100, Bangladesh
2Department of Computer Science and Engineering, University of Rajshahi,
Rajshahi, 6205, Bangladesh, Email<EMAIL_ADDRESS>
3iThree Institute, Faculty of Science, University of Technology Sydney,
Australia
4Complex Systems Research Group, Faculty of Engineering, The University of
Sydney, Darlington, NSW, 2008, Australia
5Department of Computer Science and Engineering, Jatiya Kabi Kazi Nazrul Islam
University, Mymensingh, Bangladesh
6Department of Mathematics and Statistics, Imam Mohammad Ibn Saud Islamic
University, Saudi Arabia
7Faculty of Medicine, School of Psychiatry, University of New South Wales,
Sydney, NSW, Australia
8Institute for Physical Activity and Nutrition (IPAN), Faculty of Health,
Deakin University
9Healthy Ageing Theme, Darlinghurst, NSW, The Garvan Institute of Medical
Research, Australia
10WHO Collaborating Centre on eHealth, UNSW Digital Health, School of Public
Health and Community Medicine, Faculty of Medicine, UNSW Sydney, Australia

  *

Abstract

Introduction: For COVID-19 patients’ accurate prediction of disease severity
and mortality risk would greatly improve care delivery and resource
allocation. There are many patient-related factors, such as pre-existing
comorbidities that affect disease severity. Since rapid automated profiling of
peripheral blood samples is widely available, we investigated how such data
from the peripheral blood of COVID-19 patients might be used to predict
clinical outcomes.

Methods: We thus investigated such clinical datasets from COVID-19 patients
with known outcomes by combining statistical comparison and correlation
methods with machine learning algorithms; the latter included decision tree,
random forest, variants of gradient boosting machine, support vector machine,
K-nearest neighbour and deep learning methods.

Results: Our work revealed several clinical parameters measurable in blood
samples, which discriminated between healthy people and COVID-19 positive
patients and showed predictive value for later severity of COVID-19 symptoms.
We thus developed a number of analytic methods that showed accuracy and
precision for disease severity and mortality outcome predictions that were
above 90%.

Conclusions: In sum, we developed methodologies to analyse patient routine
clinical data which enables more accurate prediction of COVID-19 patient
outcomes. This type of approaches could, by employing standard hospital
laboratory analyses of patient blood, be utilised to identify, COVID-19
patients at high risk of mortality and so enable their treatment to be
optimised.

KEYWORDS: COVID-19; Blood Samples; Machine learning; Statistical analysis
*Corresponding Authors: Mohammad Ali Moni (m.moni@unsw.edu.au), Valsama Eapen<EMAIL_ADDRESS>

## Introduction

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has caused the
current pandemic of coronavirus disease-19 (COVID-19), which first emerged as
an outbreak in December 2019 in the Chinese province of Hubei [1]. The
management of COVID-19 infected patients remains problematic and
controversial, although this is to be expected in such a recently emerged
disease. The first symptoms of COVID-19 resemble those of many other
infections and inflammatory conditions that affect the respiratory system and
include fever, sneezing and rhinitis, persistent cough and fatigue with
general aches and pains [2]. However, an infected patient can rapidly develop
further more severe symptoms which can be life-threatening and require
intensive care intervention; these include pneumonia, severe shortage of
breath, diarrhoea, dispersed thromboses and vascular inflammation [3]. A
further issue in caring for COVID-19 patients is the presence of comorbidities
that interact with COVID-19, particularly pulmonary and vascular conditions,
that can make the patient prognosis far worse. This is an important
consideration given the current lack of availability of a vaccine or effective
therapy for COVID-19. However, there have been notable advances in treating
patients with advanced disease and indeed the common recognition of the
importance that the primary defenses against COVID-19 are public health
measures have reduced its spread.

Intensive care units (ICUs) are key to improving the survival of patients with
severe COVID-19, providing oxygen, 24-hour monitoring and care and assisted
ventilation when needed. Therefore thus, ICU beds are a precious resource in
locations where COVID-19 case numbers are high [4]. Therefore, allocating
hospital wards or ICU beds for infected patients require rapid decision-making
processes, both to utilise resources efficiently and reduce patient suffering
and mortality. In many parts of the world, stressed care systems face
significant difficulty in deciding on ICU bed allocation, so a smart,
automated system could be useful to improve care and resource allocation. The
World Health Organization has recommended that all suspected COVID-19 patients
be tested by reverse transcription-polymerase chain reaction (RT-PCR) based
diagnosis methods that directly detect viral RNA [5]. Testing by approaches
other than RT-PCR does not yet show acceptable accuracy. However, RT-PCR tests
can take many hours or days to finalise the test outcomes by which time the
health condition and infectious status of confirmed patients may deteriorate.
Rather than seeking a new single rapid test that improves on RT-PCR, an
alternative approach could be to use results from many different profiling
tests that are already available and which can be performed quickly by
existing equipment [6, 35-36]. How best to use the resulting such
multidimensional data is currently controversial.

Rapid blood and serology testing of clinical samples by current equipment
enables monitoring of many peripheral blood parameters of interest, some of
which indicate changes in organ functions and are used to diagnose a range of
conditions diseases [7]. This raises the possibility that such profiling of
blood samples could provide predictive information about the disease
trajectory and risk of comorbidities for patients with COVID-19. Some data is
already used in physician deliberations, but the many available test
parameters suggest that an agnostic statistical or machine learning approach
would improve the quality of those decisions. We, therefore, undertook a
comprehensive assessment that examined the utility of a range of statistical
and ML approaches. Indeed, our work identified algorithms that showed
significantly improved outcome estimates. This work has, therefore, the
potential to optimise the decision processes regarding patient care by
clinicians that are under significant time and resource pressure during the
current COVID-19 pandemic situation.

## Materials and Methods

We used two different datasets in our work, the first of which included data
from 89 patients and second from 1,945 patients of confirmed positive COVID-19
cases identified by RT-PCR testing. In the first dataset [8] we use some
statistical methods like student’s t-test, chi-square test and Pearson’s
correlation to identify the most significant and associative blood parameters
that can strongly distinguish between COVID-19 positive patients and healthy
people. In the second dataset [9] we use some statistical methods and several
machine learning models to identify similar significant blood parameters that
can predict severe patients between critical and non-critical COVID-19
positive patients. Figure 1 depicts the schematic diagram of the overall
workflow of our approach.

We formularized the task of identifying severe COVID-19 patients for their
appropriate ward selection as a classification problem by training machine
learning models with features from COVID-19 patients’ data. Raw data were
collected from the datasets of interest underwent a data wrangling pipeline
including denoising, missing value imputation, transformation, normalization,
and partition. Next, several statistical comparisons and correlation methods
were adopted for feature engineering, including Student’s t-test, chi-square
test, and Pearson’s correlations. Next, each dataset was further split into
three categories based on criteria of existing Non-Communicable Disease (NCD):
with NCD, without NCD and all data. Finally, a range of state-of-the-art
machine learning methods were trained and evaluated. The algorithms used
included decision tree (DT), random forest (RF), gradient boosting machine
(GBM), extreme gradient boosting machine (XGBoost), support vector machine
(SVM), light gradient boosting machine (LGBM), k-nearest neighbours (KNN), and
artificial neural network (ANN)-based deep learning sequential models . Each
of these steps are discussed in the following subsections.

Figure 1: Proposed methodology and workflow of this research for machine
learning analysis. [NCD = Non-Communicable Disease]

### Data Collection

We have collected two different COVID-19 patients’ datasets. The first was
produced by Zenodo [8], and contains demographic information and blood sample
information from 89 COVID-19 positive patients. In this dataset, 31 patients
were alive at the point of data collection, while 58 patients had died prior
to collection. The second larger dataset was obtained from the Kaggle online
resource [9], which contains the grouped information of previous diseases,
blood sample results, and vital sign data of 1,945 COVID-19 positive patients.
The primary sources of this dataset are Brazilian hospitals that include Sirio
Libanes, São Paulo and Brasilia. The parameters of the dataset included
patient age percentiles, gender and demographic information. Some patients
have pre-existing non-communicable diseases (NCD), including hypertension and
immunocompromised status. The blood parameters examined included the
following: serum albumin, base excess arterial, base excess venous,
bicarbonate arterial, bicarbonate venous, bilirubin, blast, calcium,
creatinine, labels of free fatty acids (FFA), Gamma-glutamyl transferase
(GGT), glucose, hematocrit, haemoglobin, international normalised ratio (INR),
lactate, numbers of leukocytes, lymphocytes neutrophils, partial pressure
oxygen (PO2) arterial, PO2 venous, partial pressure carbon dioxide (PCO2)
arterial, PCO2 venous, pH arterial, pH venous, platelets, potassium, O2
saturation arterial, O2 saturation venous, sodium, aspartate aminotransferase
(AST/TGO), alanine aminotransferase (ALT/ TGP), TTPA, urea, heart rate,
respiratory rate, temperature, oxygen saturation, systolic and diastolic blood
pressure. During the feature engineering phase in our study, all of these
blood parameters were considered as features.

### Identification of significant routine blood parameters for SARS-CoV-2
infections

Figure 2: Association of blood parameters to be COVID-19 patients. Parameter
measurements and significant difference (using t-test) for different blood
parameters between the COVID-19 affected patients compared to the levels
expected in regular patients blood parameters.

### Data Processing

For the Zenodo dataset [8] that consists of 89 COVID-19 positive patients, we
first removed any unwanted parameters (e.g., ethnicity, BMI, drinking or
smoking habit, etc.). We then eliminated all those missing values, resulting
in a dataset of 70 patients. In the Sirio Libanes dataset [9] from Kaggle,
there were 1,945 individual patients with 54 types of tests. The primary
dataset contained a large number of missing values. The rationale behind the
removal of entries with missing parameter value is that, when we conducted a
pilot study with the imputation of missing values with mean, median, or
regression values, a poor predictive performance was observed. Therefore, we
eliminated entries which contained at least one missing value. This
elimination resulted in 545 sets of patient data entries in the second dataset
that contains no missing values. Among the patients of this dataset, 264 had
symptoms severe enough to be admitted into ICU. Both datasets underwent a
denoising step, where we removed unwanted strings. Standard scaling techniques
performed feature scaling that is the mean value of the data will be 0 with
variance value 1; this will be calculated as a mean value of a feature is
subtracted from original value then divided by standard deviation. After
preprocessing, we consider 545 patients data for the analysis. For the precise
study, then we divided this dataset according to whether a patient had a
coexisting non-communicable disease (NCD) or not (no NCD). We found 264 NCD
patients and 281 without NCDs patients, among which NCD and no NCD groups
included 156 and 108 patients (respectively) that were classed as displaying
severe conditions. After this data preparation and preprocessing, we have
considered all these data for the statistical analysis, and we randomly
selected 80% of the grouped patients data for model training and the rest for
model validation testing.

### Statistical methods to identify the most significant and associative blood
parameters

In the statistical analysis, we used chi-square tests for categorical
variables, Student’s t-test for continuous variables and Pearson’s correlation
among various blood samples counts. The null hypothesis was: the data from
both of the COVID-19 patient and healthy population were indistinguishable.
Significant blood parameters were chosen based on $Pvalue<0.05$, while in some
cases the selection criteria were adj. $p-value<0.05$ and absolute value log2
fold-change$(LFC)<1$. Furthermore, hierarchical clustering was conducted on
Pearson’s correlation coefficients for significant grouping parameters [10,
11, 12].

Figure 3: Correlation heat-maps among the various blood parameters examined
using the dataset of 89 patients.

### Machine learning models to classify COVID-19 patient severity

To identify a set of important blood samples as a feature selection step, we
have employed a set of machine learning (ML) algorithms using COVID-19 dataset
that included data from severe and non-severely affected patients. We chose
those ML algorithms that are supervised in performing classification tasks
with superior performance and fast execution [13, 34]. For this purpose, we
have considered a basic ensemble learning approach that is based on max
voting, averaging, and weighted-averaging and the advanced ensemble learning
algorithms works with stacking, blending, bagging, and boosting. The ensemble
learning algorithms are the combinations of one or more basic algorithms that
are high performing, efficient, effective, and easy to debug [14, 15].

We next address the parameters of ML algorithms that were used when it was
run. In the DT algorithm, we used the random state as 42, criterion as ‘gini,’
and the minimum sample split was 2. Similarly, in the RF algorithm, the
minimum sample split was two, and the number of estimators was 100. The SVM
algorithm sets a linear kernel, cache size 200, and 3rd degree. In the GBM
algorithm, the learning rate was 0.1, criterion ‘$friedman_{m}se$,’ and the
number of estimators 100. The learning rate in the LGBM algorithm was 0.05,
feature fraction 0.9, bagging fraction 0.8, and bagging frequency 5. In the
XGB algorithm, we used a tree-based booster, and max depth was six, learning
rates 0.1, and the number of estimators 1000. For the KNN algorithms, the
matrices we used were Minkowski; weights were uniform, and the number of
neighbours was 3 (k=3).

We have also experimented with a deep learning model, i.e. a feed-forward one-
dimensional artificial neural network (ANN), e.g., sequential model. This
model consists of an input layer, three hidden layers, and an output layer
[16]. Each layer contains a collection of parallel processing nodes, called
neurons, which takes input from the nodes of the previous layer. All the
hidden layers are ReLU (Rectified Linear Units)-activated, and the output
layer is softmax activated, providing the class probability of the input
sample. The network was trained in 1000 epochs using the stochastic gradient
descent (SGD) optimization algorithm with categorical cross-entropy loss as a
convergence indicator, and a learning rate of 0.0001.

Figure 4: Age distribution of COVID-19 patients for every patients group

### Evaluation matrices for machine learning models

We evaluated the performance of our models using precision, recall, f1 score,
the area under the receiver operator characteristics curve (AUC-ROC) and log-
loss function. The precision indicates the proportion of true positive
instances among all the positive instances [17] and the recall indicates the
proportion of what the actual true instances measure from the whole instances
[17]. Between precision and recall, we calculated the f1 score metrics for
better evaluations. This is the harmonic mean of precision and recall [17].
The AUC of a classifier is equivalent to the likelihood that the classifier
will rank a randomly picked positive value higher than a randomly picked
negative value [18]. Logarithmic Loss or log-loss value is used in multinomial
logistic regression. The classifier gives probability to each class for all
the class samples. If the p is the number of samples which are belonging to q
numbers of classes. Then the log-loss function can be calculated as:

$Log-loss=-\frac{1}{p}\sum_{i=1}^{p}\sum_{j=1}^{q}x_{ij}log(y_{ij})$ (1)

where $x_{ij}$ denotes whether p belongs to class i or not, and $y_{ij}$
denotes whether q belongs to class j. Log Loss closer to 0 demonstrates higher
accuracy. Without proper evaluation of the ML model utilizing various matrices
and relying on the accuracy, it can bring an issue when the model is conveyed
on unseen data and can bring about inaccurate predictions.

## Results

We worked with two scenarios of COVID-19 patients analysis, in the first
scenario we applied Student’s t-test and Pearson’s correlation to COVID-19
positive patients blood cell parameters and normal range of blood cell
parameters we found that both tests support predictive value of C-reactive
blood protein, procalcitonin, erythrocyte sedimentation rate, brain
natriuretic peptide, ferritin, and D-Dimer are significant for COVID-19
positive patients. In the second scenario, we accounted for only COVID-19
patients for severity calculation. We also applied two different analysis
approaches. These were Student’s t-test, the other a machine learning method
of these approaches found that respiratory rate, lactate, blood pressure
(systolic and diastolic), haemoglobin, hematocrit, base excess venous and
arterial, neutrophils, albumin, urea, platelet count and potassium were good
indicators of patient disease severity and a small set of a predictor of
COVID-19 severity measurements.

The demographic information for the patient data is shown in Table 1,
comparing the patients with severe and non-severe symptoms. The patients
(n=545) included 198 (36.33%) were female, 257 (47.16%) were above 65 years of
age, and 264 (48.44%) were admitted into ICU. Among the group that included
only patients with no NCDs (n=281), 107 (38.08%) were female, and 108 (38.43%)
patients were admitted to ICU. Moreover, in the patient group that had one or
more NCDs (n=264), 167 (63.26%) patients are over 65 years old, and 156
(59.09%) were admitted into the ICU beds. The age distribution is shown in
Figure 4.

Figure 5: Association of blood parameters with the severity of the COVID-19
disease. Association and significant difference (using t-test) for the
different blood parameters between the severe COVID-19 affected patients
compared to the non-severe COVID-19 patient blood parameters.

Our first dataset contained 89 confirmed COVID-19 positive patient blood
parameters. We hence performed a Student’s t-test of tested blood parameters
to compare the expected range values (shown in Figure 2) with COVID-19
patients from the first dataset. According to the t-test analyses, the more
likely informative blood parameters were immature grans (abs), haemoglobin
A1C, fibrinogen, lipase, albumin, red blood cell count, estimated glomerular
filtration rate, C-reactive protein and partial thromboplastin time. The
combination of t-test and log2 fold changes (LFC) indicated that the eight
(08) most significant candidate predictive parameters for COVID-19 positive
status were lipase, C-reactive protein, procalcitonin level, erythrocyte
sedimentation rate, brain natriuretic peptide, ferritin, D-Dimer, and creatine
kinase level, which all showed p-values less than 0.001 and absolute $LFC>1$.

| All patients (545) | Without NCD (281) | With NCD (264)
---|---|---|---
| n(%) | n(%) | n(%)
Age above 65 years | 257(47.16) | 90(32.03) | 167(63.26)
Age Percentile |  |  |
10 | 115(21.1) | 63(22.42) | 52(19.7)
20 | 58(10.64) | 41(14.59) | 17(6.44)
30 | 55(10.09) | 38(13.52) | 17(6.44)
40 | 60(11.01) | 39(13.88) | 21(7.96)
50 | 50(9.17) | 22(7.83) | 28(10.61)
60 | 53(9.73) | 24(8.54) | 29(10.98)
70 | 55(10.09) | 26(9.25) | 29(10.98)
80 | 49(8.99) | 16(5.7) | 33(12.5)
90 | 50(9.17) | 12(4.27) | 38(14.39)
$\geq$100 | 54(9.91) | 15(5.34) | 39(14.77)
Gender |  |  |
Female | 198(36.33) | 107(38.08) | 91(34.47)
Male | 347(63.67) | 174(61.92) | 173(65.53)
Admitted into ICU | 264(48.44) | 108(38.4) | 156(59.09)
Table 1: Accuracy and evaluation matrices for every patients groups

We applied Student’s t-test to the a second dataset to attempt to discriminate
severe and non-severe symptom COVID-19 positive patients by finding patients
characteristics which are associated with the target variable disease
severity; the analysis results are shown in Figure 5. According to t-test
results from the most significant features of COVID-19 patients that imply the
patient severity is the patient’s hospital visit identifier, age, gender,
previous co-existing diseases with hypertension. The most significant blood
parameters according to t-test results were lactate, respiratory rate,
diastolic blood pressure, haemoglobin, hematocrit, base excess venous,
leukocytes, neutrophils, albumin, base excess arterial, urea, platelets count,
potassium and systolic blood pressure.

### Clustering and co-expression analysis

We also performed Pearson’s correlation between the different routine blood
parameters. The Pearson correlation is shown in Figure 3. And we found that
there are some hierarchical clusters of the tests that showed significance
equally for all the patients. From the total 59 blood samples, we found that
there were four (04) different most concordant clusters that are strongly
correlated with each other. The first cluster comprised pulse pressure and
systolic blood pressure. The second comprised haemoglobin, hematocrit, and red
blood cells. The third cluster was built with C-reactive protein, erythrocyte
sedimentation rate, diastolic blood pressure, and respiratory rate.
Procalcitonin levels, ferritin, and creatine kinase levels composed the fourth
cluster.

### Prediction of severe COVID-19 patients for critical treatment using
machine models

In this section, we first describe the performances of used machine learning
models and their applications. We then present the most important reduced set
of blood and physical sign parameters that can precisely discriminate the
patients with severe disease from those with non-severe disease. The reduced
collection of blood parameters, also known as significance for severe COVID-19
positive patients’.

Dataset | Matrices | RF | LGBM | SVM | DT | XGB | GBM | KNN | ANN
---|---|---|---|---|---|---|---|---|---
Combined | Accuracy | 0.89 | 0.88 | 0.84 | 0.82 | 0.88 | 0.89 | 0.84 | 0.83
| AUC | 0.89 | 0.88 | 0.84 | 0.82 | 0.88 | 0.89 | 0.84 | 0.82
| Precision | 0.9 | 0.88 | 0.84 | 0.83 | 0.91 | 0.91 | 0.81 | 0.92
| Recall | 0.9 | 0.9 | 0.88 | 0.83 | 0.86 | 0.88 | 0.93 | 0.69
| F1-Score | 0.9 | 0.89 | 0.86 | 0.83 | 0.88 | 0.89 | 0.86 | 0.79
| Log Loss | 3.8 | 4.12 | 5.39 | 6.34 | 4.12 | 3.8 | 5.39 | 6.02
With NCDs | Accuracy | 0.93 | 0.91 | 0.84 | 0.86 | 0.91 | 0.88 | 0.74 | 0.74
| AUC | 0.92 | 0.91 | 0.83 | 0.85 | 0.9 | 0.86 | 0.73 | 0.71
| Precision | 0.89 | 0.91 | 0.83 | 0.85 | 0.89 | 0.84 | 0.74 | 0.86
| Recall | 1 | 0.94 | 0.91 | 0.91 | 0.97 | 0.97 | 0.81 | 0.48
| F1-Score | 0.94 | 0.92 | 0.87 | 0.88 | 0.93 | 0.9 | 0.78 | 0.62
| Log Loss | 2.42 | 3.02 | 5.45 | 4.85 | 3.03 | 4.24 | 9.09 | 9.09
Without NCDs | Accuracy | 0.91 | 0.93 | 0.84 | 0.84 | 0.87 | 0.89 | 0.77 | 0.74
| AUC | 0.91 | 0.92 | 0.83 | 0.84 | 0.87 | 0.89 | 0.79 | 0.71
| Precision | 0.89 | 0.89 | 0.83 | 0.85 | 0.82 | 0.82 | 0.65 | 0.77
| Recall | 0.97 | 1 | 0.91 | 0.88 | 0.85 | 0.9 | 0.85 | 0.82
| F1-Score | 0.93 | 0.94 | 0.87 | 0.86 | 0.83 | 0.86 | 0.74 | 0.79
| Log Loss | 3.03 | 2.42 | 5.45 | 5.45 | 4.56 | 3.91 | 7.82 | 9.12
Table 2: Accuracy and evaluation matrices for every data groups

For the machine learning analysis for the second dataset, we applied the
respective methods and models; their performances and the evaluation matrices
are shown in Table 2. In the data group of all patients with and without NCDs,
we found that RF and GBM gave the highest testing accuracy score 89% and the
other methods and models perform with above 80% testing accuracy score. The
highest Area Under the ROC curve (AUC) score is for RF, and GBM 89% and also
other methods and models scored ethical AUC values above 80%. The highest
precision values were seen for XGB and GBM with 91%. The highest Recall values
are for KNN 93%, RF and LGBM score 90% and other methods scores above 80%. The
best F1-score is 90% for RF and others have above 80%. RF and GBM have the
lowest log loss value of 3.8%, and other methods and models also have ethical
lowest values below 7%. In this patient group, we saw that all of our applied
models achieve good performance in every evaluation matrix with above 80%
accuracy score, so in practice any of those can be employed.

In the data group of patients who have no NCDs, we found that RF performed
with the highest accuracy score of 93%, LGBM and XGB performed with 91% and
SVM, DT performed with a good accuracy score above 80%. Nevertheless, KNN and
ANN showed comparatively low accuracy scores 74%, because when we divide the
dataset the size of data being small. RF performed with the highest AUC score,
92%, LGBM 91% and XGB 90%. LGBM have the highest precision value, 91%, RF and
XGB have 89%. The highest precision values were91% for LGBM, and other methods
and models have above 80% except for KNN (74%). The highest recall values were
for RF 100%, XGB and GBM 97%, and other methods and models had above 80%,
except ANN (48%). RF scores highest F1-score 94%, XGB 93%, LGBM 92% and SVM
and DT scores 88%. However, KNN and ANN scored comparatively lowly, with 78%
and 62% respectively because of lower training sample sizes. The lowest log
loss value was 2.42% for RF, and other methods and models also perform with
good log loss values below 10%. In this patient group, we observed that
excepting KNN and ANN all of the model accuracy scores were above 80%, and the
evaluation matrix showed good model performances. So, the best performing
models could usefully be applied in clinical scenarios.

In the data group of patients who had one or more coexisting NCDs, we found
that LGBM performed with highest accuracy score 93%, and RF, GBM, XGB, SVM,
and DT performed with 91%, 89%, 87%, 84%, and 84%, respectively. KNN and ANN
performed poorly but this was due to the small amount of available data
showing 77% and 74% accuracy, respectively. The highest AUC score was 92% for
LGBM and RF, SVM, DT, XGB, GBM, KNN and ANN scored 91%, 83%, 84%, 87%, 89%,
79% and 71% respectively. RF and LGBM perform with highest precision value 89%
and other methods and models performed with good precision values above 80%
except for KNN and ANN. LGBM scores highest recall value 100%, RF 97%, GBM
90%, SVM 83%, and DT 88% and the other methods and models perform above 80%.
The highest F1-score is 94% for LGBM, and RF also performs with 93% and other
methods and models perform above 80% except for KNN and ANN. KNN and ANN
performed with 74% and 79% F1-score, respectively, but the number of training
samples were small.

Figure 6: Sorted significant and impacted blood parameters COVID-19 patients.
Artificial intelligence models predicted the blood parameters most predictive
for the severity of COVID-19 symptoms. The higher coefficient values of ML
model outcomes indicate the higher significant association of seriousness. A.
Combined patients group; B. Patients those have NCDs; C. Patients with no
NCDs; Note, the SHAP values are defined as the coefficient values of each
parameter after model training.

In Figure 6. A, we present the most crucial blood parameters data that are
highly predictive for COVID-19 patient disease severity among patients with
and without NCDs. For the combined (i.e., both with and without NCD) patient
data group, we found that RF method found significant features were
respiratory rate, lactate, diastolic blood parameters, neutrophiles, base
excess venous, systolic blood pressure, temperature, platelets, venous $O_{2}$
saturation, venous $PO_{2}$ saturation, calcium, haemoglobin, heart rate,
potassium, and TGO . LGBM found lactate, diastolic blood pressure,
neutrophils, systolic blood pressure, PO2 venous, calcium, base excess venous,
hematocrit, respiratory rate, potassium, heart rate, platelets, bilirubin,
sodium, and TGP as significant. Using SVM, the significant blood parameters
were diastolic blood pressure, heart rate, respiratory rate, systolic blood
pressure, oxygen saturation level, neutrophiles, base excess venous,
saturation $O_{2}$ venous, leukocytes, lactate, $PO_{2}$ venous, bicarbonate
venous, base excess arterial, hematocrit and PH arterial. The most predictive
blood test data for the DT algorithm were respiratory rate, neutrophiles,
glucose, INR, haemoglobin, temperature, and TGP. For the XGB algorithm,
significant blood parameters were diastolic blood pressure, lactate,
neutrophiles, $PO_{2}$ venous, calcium, systolic blood pressure, platelets,
hematocrit, respiratory rate, PCR, saturation $O_{2}$ venous, urea, TGO, base
excess venous, and lymphocytes. The most significant blood parameters for the
GBM algorithm were respiratory rate, diastolic blood pressure, systolic blood
pressure, lactate, neutrophiles, calcium, $PO_{2}$ venous, oxygen saturation
level, platelets, base excess venous, heart rate, lymphocytosis, TGO,
haemoglobin and sodium. KNN identified, lactate, respiratory rate, oxygen
saturation level, systolic blood pressure, saturation $O_{2}$ venous,
diastolic blood pressure, $PO_{2}$ venous, base excess venous, haemoglobin,
neutrophils, lymphocytosis, PH venous, saturation $O_{2}$ arterial, $PO_{2}$
arterial, and glucose as significant. The most striking predictive blood
parameters for ANN were lactate, respiratory rate, saturation $O_{2}$ venous,
diastolic blood pressure, heart rate, systolic blood pressure, potassium,
sodium, hematocrit, base excess arterial, creatinine, haemoglobin, albumin
and, $PO_{2}$ arterial.

In the data group of the patients who have one or more coexisting NCDs, the
most significant blood parameters are shown in Figure 6. B. For this data
group, the Rf method found respiratory rate, lactate, base excess venous,
neutrophils, diastolic blood pressure, calcium, leukocytes, temperature,
$PO_{2}$ venous, sodium, potassium, bicarbonate venous, systolic blood
pressure, urea and hematocrit as significant blood parameters. The LGBM method
found lactate, neutrophils, haemoglobin, creatinine, FFA, platelets,
temperature, leukocytes, calcium, systolic blood pressure, TGO, INR,
hematocrit, potassium and diastolic blood pressure as significant. The most
highly predictive blood parameters when using the SVM method was systolic
blood pressure, heart rate, oxygen saturation level, respiratory rate,
diastolic blood pressure, lymphocytosis, base excess venous, bicarbonate
venous, hematocrit, sodium, saturation O2 venous, bicarbonate arterial,
temperature, PH venous and base excess arterial. The DT method finds INR,
systolic blood pressure, and temperature were significant. The significant
blood parameters for the XGB method were lactate, neutrophils, diastolic blood
pressure, systolic blood pressure, PCR, TGO, calcium, TTPA, creatinine,
sodium, lymphocytosis, respiratory rate, platelets, potassium and urea. The
GBM method found lactate, respiratory rate, diastolic blood pressure, systolic
blood pressure, neutrophiles, oxygen saturation level, TTPA, heart rate, urea,
calcium, $PO_{2}$ venous, dimer, sodium, hematocrit, and TGO as significant.
KNN identified temperature, oxygen saturation level, hematocrit, sodium,
diastolic blood pressure, haemoglobin, potassium, systolic blood pressure,
platelets, lactate, heart rate, lymphocytosis, TTPA, calcium and creatinine as
significant. The significant blood parameters for the ANN model were lactate,
platelets, systolic blood pressure, sodium, potassium, $PO_{2}$ venous,
diastolic blood pressure, PH arterial, leukocytes, saturation $O_{2}$ venous,
heart rate, $PCO_{2}$ venous, $PO_{2}$ arterial, creatinine and neutrophils.

In the data group of patients who have no NCDs, the most significant blood
parameters are shown in Figure 6. C and described in this section. We found
that the RF method identified respiratory rate, lactate, base excess
venous,$PO_{2}$ venous, saturation $O_{2}$ venous, calcium, diastolic blood
pressure, $PCO_{2}$ venous, bicarbonate venous, haemoglobin, systolic blood
pressure, potassium, $PO_{2}$ arterial, hematocrit and leukocytes as
significant. The LGBM method found lactate, respiratory rate, systolic blood
pressure, $PO_{2}$ venous, calcium, diastolic blood pressure, hematocrit,
haemoglobin, neutrophils, potassium, base excess venous, urea, oxygen
saturation level, creatinine and leukocytes were significant. The most
important blood parameters for the SVM method were respiratory rate, diastolic
blood pressure diastolic, oxygen saturation level, systolic blood pressure,
base excess venous, temperature, lactate, haemoglobin, neutrophils,
bicarbonate venous, sodium, glucose, hematocrit and creatinine. DT found
dimer, neutrophiles and oxygen saturation level as significant components. The
most significant blood parameters for XGB method is a respiratory rate,
lactate, hematocrit, calcium, systolic blood pressure, creatinine, potassium,
PCR, diastolic blood pressure, neutrophils, haemoglobin, temperature, PH
venous, venous$O_{2}$ saturation and TGO. The GBM method found respiratory
rate, diastolic blood pressure, systolic blood pressure, lactate, calcium,
$PO_{2}$ venous, oxygen saturation level, base excess venous, hematocrit,
potassium, PH venous,$PCO_{2}$ venous, leukocytes, PCR and TGO were
significant. The KNN method identified respiratory rate, lactate, oxygen
saturation level, heart rate, venous $O_{2}$ saturation creatinine,
neutrophiles, bicarbonate venous, lymphocytosis, temperature, sodium, albumin,
bilirubin, and FFA as significant. The most significant blood parameters for
ANN were lactate, heart rate, respiratory rate, saturation $O_{2}$ venous,
platelets, albumin, potassium, base excess venous, diastolic blood pressure,
GGT, dimer, FFA, saturation $O_{2}$ and leukocytes arterial.

We also observed that beyond blood parameters, some vital signs of COVID-19
patients also predicted disease severity. Patient gender, age, coexisting
diseases (such as hypertension) were also found to be significant for these
patient data groups of data.

## Discussion

After the worldwide outbreak of COVID-19, classification of disease mortality
risk are of very great significance in prevention, and treatment allocation.
In this investigation, we found a number of blood analysis parameters that can
be used as risk factors for the assessment of likely disease severity in
COVID-19 patients. We developed predictive algorithms that utilised a large
number of blood parameters and demonstrated that these methods and the
potential to predict the disease severity of COVID-19 patients with high
accuracy.

We identified a number of features of patient data that contributed strongly
to the predicted value of the algorithms (i.e., were found to contribute to
the accuracy of all our best machine learning algorithms) some of whom were
not obvious candidate predictors. We found that the absolute value of
lymphocytes in the severe symptom group of patients was consistently lower
than that of the non-severe symptom group. The neutrophil parameters of the
severe symptom group was higher than that of the non-severe symptom group. The
latter characteristics indicated a level of immune activation heightened and
possibly playing a role in the “inflammatory storm” that is a characteristic
of COVID-19 severe symptoms which result in very high tissue and cell harm
[19]. Low lymphocyte levels may reflect impeded antibody-based immune cell
functions suspected to result in patients with severe COVID-19 susceptible to
bacterial infection [20]. The algorithm outcomes indicated that numbers of
circulating lymphocytes in the patients who developed severe symptoms was
significantly lower than those who did not have severe symptoms. In contrast,
the inclusion of neutrophils in the severe ICU patients’ was influenced
higher, which is consistent with the consequences of Qin et al. [21].

We found that the indicator factors could be reliable to predictors that
discriminated between severe and non-severe COVID-19 patients. Recent work has
uncovered the utility of routine blood parameters in the screening of COVID-19
patients. This is facilitated by the fact that blood parameter analysis is
generally fast, affordable, and promptly accessible in the same health
facility where patients are receiving treatment. The pathological tests of
COVID-19 patients identified some abnormalities in some blood parameters.
Previous published data has identified a number of altered blood parameters
inCOVID-19 patients who develop severe symptoms include in addition to the
lymphocyte and neutrophil parameters noted above, are, eosinophils, basophils,
monocytes, platelets and total leukocytes as well as serum levels of urea,
potassium, haemoglobin and C-reactive blood protein. [22, 23, 24], which
provides supportive evidence for our findings. Li et al. identified that
COVID-19 affects bacterial pneumonia in some cases of mortality [25].
Bacterial contamination also causes expanded leucocyte count and neutrophil
count, which may be linked to defective immune responses. A few patients with
COVID-19 have abnormal blood coagulation function: prothrombin time is
increased, and D-dimer is raised [19]; thrombosis is linked with expanded
platelet consumption and diminished platelet number.

Respiratory rate is one of the principal vital signs for patient symptom
severity in COVID-19. Abnormally high respiratory rates (<12 or >25
breaths/min) are also seen in a range of conditions including asthma,
heightened anxiety, pneumonia, congestive heart failure, lung disease (all of
which exacerbate COVID-19 conditions when presenting as comorbidities) and are
a significant feature in severely affected COVID-19 patients [26, 27].
Elevated heart rate is similarly a key sign [28] and may be the cause of
dizziness or shortness of breath in COVID-19 patients [29]. Blood pressure is
additionally a clinical sign for COVID-19 patients [30]. Hypoxemia is also a
sign that indicates the average below level of oxygen saturation in the blood.
The usual range of arterial oxygen is 75 to 100 (mm Hg) approximately, and in
pulse oximeter reading the expected range from 95% to 100%; below 90%
indicates patients’ condition is critical [31], and this is often observed in
COVID-19 patients who may lack other obvious symptoms, making it a
particularly dangerous feature of the disease. Related to this is the serum
lactic acid test, which is also a significant test that indicates disease
severity in COVID-19 patients. Typically the level of lactate in the blood is
very low, but when it rises it is typically associated with low oxygen levels
[32, 33].

In summary there are a number of signs and symptoms that can indicate COVID-19
is likely to become severe in a patient. What is needed is a standardised and
objective way to combine these and other less obvious predictors in a way that
can optimise patient outcomes and resource management. Our methodology,
described here and derived from a number of different machine learning
algorithms, can provide such an improved method. Indeed the fact that high
accuracy was obtained using similar predictors by different machine learning
algorithms (so there is limited sensitivity to methodology) can give
confidence that these parameters are of utility, and the approach is a sound
one.

## Conclusion

The results of our analysis indicated that there is a strong relationship
between particular abnormal blood parameters and disease severity status in
hospitalised patients with COVID-19. The primary utility of our findings is
that the subset of routine blood parameters linked to disease severity could
be used in a predictive algorithm that would better enable appropriate care to
be given before the onset of severe symptoms. This is of particular importance
in developing countries, where the hospitals where ICU beds are a limited
resource. This can be achieved using a relatively small number of currently
available hospital blood-based tests to proper utilisation of ICU resources
and identifying patients that need to be monitored closely.

Among the association between blood parameters that are able to give
predictive information regarding COVID-19 symptoms severity, the levels of
C-reactive blood protein appeared to have the strongest predictive value.
Levels of haemoglobin, procalcitonin, erythrocyte sedimentation rates, brain
natriuretic peptide, ferritin, D-Dimer, and platelets were likewise showed
significant deviation from the normal control group in this. Other parameters,
respiratory rate, lactate, blood pressure (systolic and diastolic),
hematocrit, base excess venous and arterial, neutrophils, albumin, and urea
showed less obvious deviations but clearly had predictive value. Our work
suggests that links between these parameters and COVID-19, and perhaps similar
pro-inflammatory infectious diseases are worth more detailed physiological
investigations.

There were a few limitations to our study. Firstly, the small sample size may
restrict the precision of the severity identification. Secondly, the absence
of more detailed clinical information in the datasets that were used (such as
patients’ age, sex and comorbidities), which may hinder better classification,
although it suggests that future studies with new datasets could address this
and improve on our work. Finally, the disease severity and mortality of
COVID-19 varies significantly from country to country, for reasons that are
very poorly understood but suggests that this type of predictive analysis
should be carried out on data from other parts of the world to improve
performance of the algorithm. Nevertheless, we hope our study can be used by
practitioners, facilitates sound policy-makers to improve resource allocation
and patient outcomes for COVID-19 patients.

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