Predict Mortality in Patients Infected with COVID-19 Virus Based on Observed Characteristics of the Patient using Logistic Regression

Josephus, Bernhard O.; Nawir, Ardianto H.; Wijaya, Evelyn; Moniaga, Jurike V.; Ohyver, Margaretha

The spread of COVID-19 has made the world a mess. Up to this day, 5,235,452 cases confirmed worldwide with 338,612 death. One of the methods to predict mortality risk is machine learning algorithm using medical features, which means it takes time. Therefore, in this study, Logistic Regression is modeled by training 114 data and used to create a prediction over the patient’s mortality using nonmedical features. The model can help hospitals and doctors to prioritize who has a high probability of death and triage patients especially when the hospital is overrun by patients. The model can accurately predict with more than 90% accuracy achieved. Further analysis found that age is the most important predictor in the patient’s mortality rate. Using this model, the death rate caused by COVID-19 could be reduced.