Predicting the Covid-19 Mortality Among Iranian Patients Using Tree-Based Models: A Cross-Sectional Study Publisher



Aghakhani A¹ ; Shoshtarian Malak J² ; Karimi Z¹ ; Vosoughi F³ ; Zeraati H¹ ; Yekaninejad MS¹ Show Affiliations
Source: Health Science Reports Published:2023

Abstract

Background and Aims: To explore the use of different machine learning models in prediction of COVID-19 mortality in hospitalized patients. Materials and Methods: A total of 44,112 patients from six academic hospitals who were admitted for COVID-19 between March 2020 and August 2021 were included in this study. Variables were obtained from their electronic medical records. Random forest-recursive feature elimination was used to select key features. Decision tree, random forest, LightGBM, and XGBoost model were developed. Sensitivity, specificity, accuracy, F-1 score, and receiver operating characteristic (ROC)-AUC were used to compare the prediction performance of different models. Results: Random forest-recursive feature elimination selected following features to include in the prediction model: Age, sex, hypertension, malignancy, pneumonia, cardiac problem, cough, dyspnea, and respiratory system disease. XGBoost and LightGBM showed the best performance with an ROC-AUC of 0.83 [0.822−0.842] and 0.83 [0.816−0.837] and sensitivity of 0.77. Conclusion: XGBoost, LightGBM, and random forest have a relatively high predictive performance in prediction of mortality in COVID-19 patients and can be applied in hospital settings, however, future research are needed to externally confirm the validation of these models. © 2023 The Authors. Health Science Reports published by Wiley Periodicals LLC.