Citation: Syed, A.H.; Khan, T.;
Alromema, N. A Hybrid Feature
Selection Approach to Screen a Novel
Set of Blood Biomarkers for Early
COVID-19 Mortality Prediction.
Diagnostics 2022, 12, 1604. https://
doi.org/10.3390/diagnostics12071604
Academic Editors: Keun Ho Ryu and
Sameer Antani
Received: 28 May 2022
Accepted: 29 June 2022
Published: 30 June 2022
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Article
A Hybrid Feature Selection Approach to Screen a Novel Set of
Blood Biomarkers for Early COVID-19 Mortality Prediction
Asif Hassan Syed
1,
* , Tabrej Khan
2
and Nashwan Alromema
1
1
Department of Computer Science, Faculty of Computing and Information Technology Rabigh (FCITR),
King Abdulaziz University, Jeddah 22254, Saudi Arabia; nalromema@kau.edu.sa
2
Department of Information Systems, Faculty of Computing and Information Technology Rabigh (FCITR),
King Abdulaziz University, Jeddah 22254, Saudi Arabia; tkamin@kau.edu.sa
* Correspondence: shassan1@kau.edu.sa
Abstract:
The increase in coronavirus disease 2019 (COVID-19) infection caused by severe acute
respiratory syndrome coronavirus 2 (SARS-CoV-2) has placed pressure on healthcare services world-
wide. Therefore, it is crucial to identify critical factors for the assessment of the severity of COVID-19
infection and the optimization of an individual treatment strategy. In this regard, the present study
leverages a dataset of blood samples from 485 COVID-19 individuals in the region of Wuhan, China
to identify essential blood biomarkers that predict the mortality of COVID-19 individuals. For this
purpose, a hybrid of filter, statistical, and heuristic-based feature selection approach was used to
select the best subset of informative features. As a result, minimum redundancy maximum relevance
(mRMR), a two-tailed unpaired t-test, and whale optimization algorithm (WOA) were eventually se-
lected as the three most informative blood biomarkers: International normalized ratio (INR), platelet
large cell ratio (P-LCR), and D-dimer. In addition, various machine learning (ML) algorithms (ran-
dom forest (RF), support vector machine (SVM), extreme gradient boosting (EGB), naïve Bayes (NB),
logistic regression (LR), and k-nearest neighbor (KNN)) were trained. The performance of the trained
models was compared to determine the model that assist in predicting the mortality of COVID-19
individuals with higher accuracy, F1 score, and area under the curve (AUC) values. In this paper, the
best performing RF-based model built using the three most informative blood parameters predicts
the mortality of COVID-19 individuals with an accuracy of
0.96 ± 0.062
, F1 score of
0.96 ± 0.099
,
and AUC value of 0.98
±
0.024, respectively on the independent test data. Furthermore, the perfor-
mance of our proposed RF-based model in terms of accuracy, F1 score, and AUC was significantly
better than the known blood biomarkers-based ML models built using the Pre_Surv_COVID_19
data. Therefore, the present study provides a novel hybrid approach to screen the most informative
blood biomarkers to develop an RF-based model, which accurately and reliably predicts in-hospital
mortality of confirmed COVID-19 individuals, during surge periods. An application based on our
proposed model was implemented and deployed at Heroku.
Keywords:
COVID-19; blood biomarkers; hybrid-feature selection; filter-based feature selection;
two-tailed unpaired t-test; meta-heuristic method; machine learning models; mortality risk prediction
1. Introduction
1.1. Rationale for Developing COVID-19 Mortality Risk Prediction Technique
COVID-19 presents a broad spectrum of clinical manifestations, ranging from asymp-
tomatic to critically ill COVID-19 individuals, with progressive respiratory failure [
1
–
7
].
At this pandemic stage, an unexpected increase in COVID-19 cases has placed immense
pressure on health care services, leading to a shortage of intensive care resources. Most of
the individuals admitted to hospitals will survive, but some individuals develop severe
respiratory failures requiring ventilators. In addition, many of these individuals on venti-
lators succumb to their rapidly progressive respiratory dysfunctions. Identifying crucial
Diagnostics 2022, 12, 1604. https://doi.org/10.3390/diagnostics12071604 https://www.mdpi.com/journal/diagnostics
