Predicting the mortality of patients with Covid‐19: A machine learning approach. Issue 4 (30th March 2023)

• Record Type: Journal Article
• Authors: Emami, Hassan; Rabiei, Reza; Sohrabei, Solmaz; Atashi, Alireza
• Is Part Of: Health science reports. Volume 6:Issue 4(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2023-03-30
• Subjects: Covid‐19 -- gradient boosting tree -- machine learning -- random forest -- support vector machine; 610
• DOI: 10.1002/hsr2.1162
• Languages: English
• ISSNs: 2398-8835

Abstract

Abstract: Background and Aims: Infection with Covid‐19 disease can lead to mortality in a short time. Early prediction of the mortality during an epidemic disease can save patients' lives through taking timely and necessary care interventions. Therefore, predicting the mortality of patients with Covid‐19 using machine learning techniques can be effective in reducing mortality rate in Covid‐19. The aim of this study is to compare four machine‐learning algorithm for predicting mortality in Covid‐19 disease. Methods: The data of this study were collected from hospitalized patients with COVID‐19 in five hospitals settings in Tehran (Iran). Database contained 4120 records, about 25% of which belonged to patients who died due to Covid‐19. Each record contained 38 variables. Four machine‐learning techniques, including random forest (RF), regression logistic (RL), gradient boosting tree (GBT), and support vector machine (SVM) were used in modeling. Results: GBT model presented higher performance compared to other models (accuracy 70%, sensitivity 77%, specificity 69%, and the ROC area under the curve 0.857). RF, RL, and SVM models with the ROC area under curve 0.836, 0.818, and 0.794 were in the second and third places. Conclusion: Considering the combination of multiple influential factors affecting death Covid‐19 can help in early prediction and providing a better care plan. In addition, using different modeling on data can be useful for physician in providing appropriate care.