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

Shanbehzadeh, Mostafa and Haghiri, Hamideh and Afrash, Mohammad Reza and Amraei, Morteza (2022) Comparison of Machine Learning Tools for the Prediction of ICU Admission in COVID-19 Hospitalized Patients. Shiraz E Medical Journal.

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Abstract

Background: The rapid coronavirus disease 2019 (COVID-19) outbreak has overwhelmed many healthcare systems worldwide and put them at the edge of collapsing. As the capacity of intensive care units (ICUs) is limited, deciding on the proper allocation of required resources is crucial. Objectives: This study aimed to create a machine learning (ML)-based predictive model of ICU admission among COVID-19 in-hospital patients at the initial presentation. Methods: This retrospective study was conducted on 1225 laboratory-confirmed COVID-19 hospitalized patients during January 9, 2020-January 20, 2021. The top clinical parameters contributing to COVID-19 ICU admission were identified based on a correlation coefficient at P-value < 0.05. Next, the predictive models were constructed using five ML algorithms. Finally, to evaluate the perfor-mances of models, the metrics derived from the confusion matrix, classification error, and receiver operating characteristic were calculated. Results: Following feature selection, a total of 11 parameters were selected as the top predictors to build the prediction models. The results showed that the best performance belonged to the random forest (RF) algorithm with the mean accuracy of 99.5%, mean specificity of 99.7%, mean sensitivity of 99.4%, Kappa metric of 95.7%, and root mean squared error of 0.015. Conclusions: The ML algorithms, particularly RF, enable a reasonable level of accuracy and certainty in predicting disease progres-sion and ICU admission for COVID-19 patients. The proposed models have the potential to inform frontline clinicians and health authorities with quantitative tools to assess illness severity and optimize resource allocation under time-sensitive and resource-constrained situations

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