Analyzing Mortality Risk of COVID-19 Patients Using Machine Learning

Abstract— The abrupt increase in the number of illnesses and high fatality rates during the covid-19 epidemic have put enormous strain on public health services. As a result, identifying the main parameters for mortality prediction is essential for improving patient treatment plans. Early detection of patient mortality issues can help to avert death by ensuring optimal resource allocation and treatment planning. The primary goal of this research is to swiftly identify the Severe Covid-19 patient by looking at demographics, comorbidities, admission, laboratory data, admission medicines, admission oxygen therapy prescriptions, discharge, and mortality data. Various machine learning models (linear regression, decision trees, and KNN) were trained and their performance compared to determine the model that consistently achieves high accuracy during the disease's days.