A machine learning model for predicting recovery rates of COVID-19 patients

Abstract

During disease epidemics, any trial to improve healthcare systems entails preserving lives. Therefore, predicting which patients are at high risk becomes critical and challenging when confronted with a novel virus. The recent COVID-19 changed many people’s perspectives on how to approach diseases. According to the lack of medical resources, it is important to identify the patients who need instant medical care. This research proposes a machine learning model to identify high-risk patients that require specific medical attention. Specifically, extreme gradient boosting (XGboost), random forest (RF), and logistic regression (LR) are used in the ensemble method to classify COVID-19 patients at high risk. The dataset consists of 361 medical records for severe COVID-19 patients which have included 195 survivors. The most correlated features (neutrophils (%), hypersensitive c-reactive protein, lactate dehydrogenase, age, procalcitonin, and neutrophils count) are selected to be used in classification. Different machine learning classifiers are applied to the mentioned dataset to find out the optimum classifiers to be used in the ensemble method. 98% is the most optimal accuracy achieved with the proposed model.