Risk factor analysis and multiple predictive machine learning models for mortality in COVID-19: a multicenter and multi-ethnic cohort study

BACKGROUND: The COVID-19 pandemic presents a significant challenge to the global healthcare system. Implementing timely, accurate, and cost-effective screening approaches is crucial in preventing infections and saving lives by guiding disease management. OBJECTIVES: The study aimed to use machine learning algorithms to analyze clinical features from routine clinical data to identify risk factors and predict the mortality of COVID-19. METHODS: The data used in this research was originally collected for the study titled "Neurologic Syndromes Predict Higher In-Hospital Mortality in COVID-19". A total of 4711 patients with confirmed COVID-19 were enrolled consecutively from four hospitals. Three machine learning models, including RF, PLS-DA, and SVM, were used to find risk factors and predict COVID-19 mortality. RESULTS: The predictive models were developed based on three machine learning algorithms. The RF model was trained with 20 variables and had a ROC value of 0.859 (95%CI: 0.804-0.920). The PLS-DA model was trained with 20 variables and had a ROC value of 0.775 (95%CI: 0.694-0.833). The SVM model was trained with 10 variables and had a ROC value of 0.828 (95%CI: 0.785-0.865). The 9 variables that were present in all three models were age, PCT, ferritin, CRP, troponin, BUN, MAP, AST, and ALT. CONCLUSION: This study developed and validated three machine learning prediction models for COVID-19 mortality based on accessible clinical features. The RF model showed the best performance among the three models. The 9 variables identified in the models may warrant further investigation as potential prognostic indicators of severe COVID-19.