Application of Machine Learning Models for Predicting COVID-۱۹ Mortality: A Retrospective Cohort Study Using Random Forest Algorithm

Background: The COVID-۱۹ pandemic has posed immense challenges to healthcare systems worldwide, making it critical to identify predictors of patient mortality. Machine learning algorithms have shown great promise in predicting outcomes for COVID-۱۹ patients based on clinical and laboratory data. This study aimed to evaluate the performance of three machine learning models—Random Forest, Support Vector Machine (SVM), and Logistic Regression—in predicting mortality among COVID-۱۹ patients using clinical and laboratory data.Methods: A retrospective cohort study was conducted on ۲,۵۰۰ COVID-۱۹ patients admitted to three major hospitals in Tehran, Iran, between ۲۰۲۰ and ۲۰۲۱. Demographic, clinical, and laboratory data were collected. Machine learning models were trained to predict mortality, and their performance was evaluated using the area under the curve (AUC), sensitivity, specificity, and positive predictive value (PPV). Model validation was performed using ۱۰-fold cross-validation and an ۸۰-۲۰ train-test split to ensure robustness and generalizability.Results: The Random Forest model outperformed both SVM and Logistic Regression with an AUC of ۸۳.۳%, sensitivity of ۶۳.۰%, and specificity of ۹۰.۵%. Important predictors of mortality included age, gender, comorbidities (such as diabetes, ischemic heart disease, and cancer), ICU admission, and laboratory markers (e.g., ALT, white blood cell count, and creatinine levels).Conclusion: The Random Forest algorithm demonstrated superior predictive performance compared to SVM and Logistic Regression in predicting mortality among COVID-۱۹ patients. These findings suggest that machine learning models, particularly Random Forest, can be instrumental in identifying high-risk patients and supporting clinical decision-making.

Keywords:

COVID-۱۹ mortality , Machine Learning , Random forest