Comparison of machine-learning algorithms efficiency to build a predictive model for mortality risk in COVID-19 hospitalized patients

Mostafa Shanbehzadeh; Ali Valinejadi; Ramin Afrah; Hadi KazemiArpanahi; Azam Orooji; Mohammadreza Kaffashian

doi:10.5812/koomesh-154100

Koomesh

Journal of Semnan University of Medical Sciences

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Comparison of machine-learning algorithms efficiency to build a predictive model for mortality risk in COVID-19 hospitalized patients

Author(s):

Mostafa Shanbehzadeh¹,

Ali Valinejadi²,

Ramin Afrah¹,

Hadi KazemiArpanahi^3,*,

Azam Orooji⁴,

Mohammadreza Kaffashian⁵

1Dept.of Biomedical Engineering, Faculty of Advanced Technologies, Isfahan University of Medical Sciences, Isfahan, Iran

2Dept.of Health Information Technology, School of Allied Medical Sciences, Semnan University of Medical Sciences, Semnan, Iran

3Dept.of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran

4Dept.of Advanced Technologies, Faculty of Medicine, North Khorasan University of Medical Science (NKUMS), Bojnourd, Iran

5Dept.of Physiology, Faculty of Medicine, Ilam University of Medical Sciences, Ilam, Iran

Koomesh:Vol. 24, issue 1; e154100

Published online:Mar 24, 2022

Article type:Research Article

How to Cite:Shanbehzadeh M, Valinejadi A, Afrah R, KazemiArpanahi H, Orooji A, et al. Comparison of machine-learning algorithms efficiency to build a predictive model for mortality risk in COVID-19 hospitalized patients. koomesh. 2022;24(1):e154100. doi:

Abstract

Introduction: The rapid worldwide outbreak of SARS-CoV-2 has posed serious and unprecedented challenges to healthcare systems in predicting disease behavior and outcomes. To overcome these challenges or ambiguities, this study aimed to create and validate several predictive models using of selected ML algorithms to stratify the mortality risk in COVID-19 hospitalized patients and choice the best performing algorithm. Materials and Methods: Data of 1224 hospitalized patients with COVID-19 diagnosis based on the findings of the confirmed-laboratory test were extracted from the Ilam COVID-19 registry (Ilam CoV reg) database. Then the most important clinical parameters in the COVID-19 mortality were identified and used as inputs of the selected ML algorithms, including K-Nearest Network (KNN), Support Vector Machine (SVM), Logistic Regression (LR) and Random Forest (RF). Finally, the performance of the developed models was compared based on different confusion matrix evaluation criteria and the most appropriate predictive model was determined. Results: A total of 17 parameters were identified as the most influential clinical variables in the mortality of COVID-19. By comparing the performance of ML algorithms according to various evaluation criteria, the KNN algorithm with precision of 94.21%, accuracy of 93.74%, recall of 100%, F-measure of 93.2% and ROC of 92.23%, yielded better performance than other developed algorithms. Conclusion: KNN enables a reasonable level of accuracy and certainty in predicting the mortality of patients with COVID-19 and potentially facilitates identifing high risk patients and, inform proper interventions by the clinicians

Keywords

COVID-19

SARS-CoV-2

Artificial Intelligence

Machine Learning

Data Mining

Comparison of Two Statistical Models for Predicting Mortality in COVID-19 Patients in Iran

Raoof Nopour,

Leila Erfannia,

Nahid Mehrabi,

Mehrnaz Mashoufi,

Abdollah Mahdavi,

Mostafa Shanbehzadeh

Nopour R, Erfannia L, Mehrabi N, Mashoufi M, Mahdavi A, et al. Comparison of Two Statistical Models for Predicting Mortality in COVID-19 Patients in Iran. Shiraz E-Med J. 2022;23(6):e119172. doi: https://doi.org/10.5812/semj.119172

5

Feb

2022

Comparison of Machine Learning Tools for the Prediction of ICU Admission in COVID-19 Hospitalized Patients

Mostafa Shanbehzadeh,

Hamideh Haghiri,

Mohammad Reza Afrash,

Morteza Amraei,

Leila Erfannia,

Hadi Kazemi-Arpanahi

Shanbehzadeh M, Haghiri H, Afrash MR, Amraei M, Erfannia L, et al. Comparison of Machine Learning Tools for the Prediction of ICU Admission in COVID-19 Hospitalized Patients. Shiraz E-Med J. 2022;23(5):e117849. doi: https://doi.org/10.5812/semj.117849

12

Oct

2024

Triage of Patients with COVID-19: Using Ensemble Learning Method for Risk Factor Analysis and Death Prediction

Neda Sadat,

Sharareh R. Niakan Kalhori,

Shahrzad Darvishi,

Jamileh Kiani,

Farhad Abbasi,

Batool Amiri

,et al.

Sadat N, R. Niakan Kalhori S, Darvishi S, Kiani J, Abbasi F, et al. Triage of Patients with COVID-19: Using Ensemble Learning Method for Risk Factor Analysis and Death Prediction. koomesh. 2024;26(1):e150060. doi: https://doi.org/10.69107/koomesh-150060

4

Jan

2025

Evaluating the Application of Machine Learning in Predicting the Mortality of Hospitalized COVID-19 Patients Using the Confusion Matrix and the Matthews Correlation Coefficient

Maryam Salari,

Seyed Masoud Sadati,

Alireza Sedaghat,

Bita Abbasi,

Seyed Amir Zamanpour,

Rozita Khodashahi

,et al.

Salari M, Sadati SM, Sedaghat A, Abbasi B, Zamanpour SA, et al. Evaluating the Application of Machine Learning in Predicting the Mortality of Hospitalized COVID-19 Patients Using the Confusion Matrix and the Matthews Correlation Coefficient. Arch Clin Infect Dis. 2025;20(2):e150150. doi: https://doi.org/10.5812/archcid-150150

24

Jan

2022

Early Identification of COVID-19 Progression to Its Severe Form Using Artificial Intelligence

Lei Yuan,

Jia Chen,

Hui Feng,

Junwei Lv,

Xuefang Lu,

Mengyao Ji

Yuan L, Chen J, Feng H, Lv J, Lu X, et al. Early Identification of COVID-19 Progression to Its Severe Form Using Artificial Intelligence. I J Radiol. 2022;19(1):e112562. doi: https://doi.org/10.5812/iranjradiol.112562

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Author(s):

Mostafa Shanbehzadeh:[PubMed][Scholar]
Ali Valinejadi:[PubMed][Scholar]
Ramin Afrah:[PubMed][Scholar]
Hadi KazemiArpanahi:[PubMed][Scholar]
Azam Orooji:[PubMed][Scholar]
Mohammadreza Kaffashian:[PubMed][Scholar]

Journal of Semnan University of Medical Sciences

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Comparison of machine-learning algorithms efficiency to build a predictive model for mortality risk in COVID-19 hospitalized patients

Abstract

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