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 health care 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 were 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 random forest (RF), partial least squares discriminant analysis (PLS-DA), and support vector machine (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 receiver operating characteristic (ROC) value of 0.859 (95% confidence interval [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 nine variables that were present in all three models were age, procalcitonin, ferritin, C-reactive protein, troponin, blood urea nitrogen, mean arterial pressure, aspartate transaminase, and alanine transaminase.

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 nine variables identified in the models may warrant further investigation as potential prognostic indicators of severe COVID-19.