Repository of Research and Investigative Information

Repository of Research and Investigative Information

Ilam University of Medical Sciences

Comparing machine learning algorithms for predicting COVID-19 mortality

Fri Oct 7 02:21:10 2022

(2022) Comparing machine learning algorithms for predicting COVID-19 mortality. Bmc Medical Informatics and Decision Making. p. 12.

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Abstract

Background The coronavirus disease (COVID-19) hospitalized patients are always at risk of death. Machine learning (ML) algorithms can be used as a potential solution for predicting mortality in COVID-19 hospitalized patients. So, our study aimed to compare several ML algorithms to predict the COVID-19 mortality using the patient's data at the first time of admission and choose the best performing algorithm as a predictive tool for decision-making. Methods In this study, after feature selection, based on the confirmed predictors, information about 1500 eligible patients (1386 survivors and 144 deaths) obtained from the registry of Ayatollah Taleghani Hospital, Abadan city, Iran, was extracted. Afterwards, several ML algorithms were trained to predict COVID-19 mortality. Finally, to assess the models' performance, the metrics derived from the confusion matrix were calculated. Results The study participants were 1500 patients; the number of men was found to be higher than that of women (836 vs. 664) and the median age was 57.25 years old (interquartile 18-100). After performing the feature selection, out of 38 features, dyspnea, ICU admission, and oxygen therapy were found as the top three predictors. Smoking, alanine aminotransferase, and platelet count were found to be the three lowest predictors of COVID-19 mortality. Experimental results demonstrated that random forest (RF) had better performance than other ML algorithms with accuracy, sensitivity, precision, specificity, and receiver operating characteristic (ROC) of 95.03, 90.70, 94.23, 95.10, and 99.02, respectively. Conclusion It was found that ML enables a reasonable level of accuracy in predicting the COVID-19 mortality. Therefore, ML-based predictive models, particularly the RF algorithm, potentially facilitate identifying the patients who are at high risk of mortality and inform proper interventions by the clinicians.

Item Type: Article
Creators:
CreatorsEmail
Moulaei, K.UNSPECIFIED
Shanbehzadeh, M.UNSPECIFIED
Mohammadi-Taghiabad, Z.UNSPECIFIED
Kazemi-Arpanahi, H.UNSPECIFIED
Keywords: COVID-19 Coronavirus Machine learning Artificial intelligence Prediction hospital mortality pneumonia disease Medical Informatics
Divisions:
Page Range: p. 12
Journal or Publication Title: Bmc Medical Informatics and Decision Making
Journal Index: ISI
Volume: 22
Number: 1
Identification Number: https://doi.org/10.1186/s12911-021-01742-0
Depositing User: مهندس مهدی شریفی
URI: http://eprints.medilam.ac.ir/id/eprint/3848

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