Investigating the performance of machine learning algorithms in predicting the survival of COVID‐19 patients: A cross section study of Iran - Yazdani, Azita; Bigdeli, Somayeh Kianian; Zahmatkeshan, Maryam (Health science reports, 2023)
Background and Aims Like early diagnosis, predicting the survival of patients with Coronavirus Disease 2019 (COVID‐19) is of great importance. Survival prediction models help doctors be more cautious to treat the patients who are at high risk of dying because of medical conditions. This study aims to predict the survival of hospitalized patients with COVID‐19 by comparing the accuracy of machine learning (ML) models. Methods It is a cross‐sectional study which was performed in 2022 in Fasa city in Iran country. The research data set was extracted from the period February 18, 2020 to February 10, 2021, and contains 2442 hospitalized patients' records with 84 features. A comparison was made between the efficiency of five ML algorithms to predict survival, includes Naive Bayes (NB), K‐nearest neighbors (KNN), random forest (RF), decision tree (DT), and multilayer perceptron (MLP). Modeling steps were done with Python language in the Anaconda Navigator 3 environment. Results Our findings show that NB algorithm had better performance than others with accuracy, precision, recall, F‐score, and area under receiver operating characteristic curve of 97%, 96%, 96%, 96%, and 97%, respectively. Based on the analysis of factors affecting survival, heart disease, pulmonary diseases and blood related disease were the most important disease related to death. Conclusion The development of software systems based on NB will be effective to predict the survival of COVID‐19 patients Key points We evaluated the role of clinical data in the survival of hospitalized COVID‐19 patients. NB classifier can classify all the cases of death correctly and its False Negative Rate is zero. History of high blood pressure and heart disease was important underlying diseases factors in the survival of COVID‐19 patients.