Machine-learning models for predicting survivability in COVID-19 patients

COVID-19 is a disease currently ravaging the world, bringing unprecedented health and economic challenges to several nations. There are presently close to five million reported cases in over 200 countries with fatalities numbering over 300,000 persons. This study presents machine-learning models for the prediction and visualization of the significant factors that determine the survivability of COVID-19 patients. This study develops prediction models using a decision tree, logistic regression (LR), gradient boosting, and LR algorithms to identify the significant factors and predict the survivability of COVID-19 patients. The results of the simulation showed that the LR model had the lowest prediction accuracy. The other three showed over 95% correct accuracy and indicated that the essential factors in determining patients' survivability were underlying health conditions and age. The findings of this study agreed with the medical claims that patients with underlying health challenges and those advanced in age are liable to have complications; hence, providing a research-based credence to this belief. This proposed model thus serves as a decision support system for the management of COVID-19 patients, as well as predicts a patient’s chances of survival at the first presentation at the hospitals.