Machine Learning-Based Model to Predict the Disease Severity and Outcome in COVID-19 Patients

The novel coronavirus (COVID-19) outbreak produced devastating effects on the global economy and the health of entire communities Although the COVID-19 survival rate is high, the number of severe cases that result in death is increasing daily A timely prediction of at-risk patients of COVID-19 with precautionary measures is expected to increase the survival rate of patients and reduce the fatality rate This research provides a prediction method for the early identification of COVID-19 patient's outcome based on patients' characteristics monitored at home, while in quarantine The study was performed using 287 COVID-19 samples of patients from the King Fahad University Hospital, Saudi Arabia The data were analyzed using three classification algorithms, namely, logistic regression (LR), random forest (RF), and extreme gradient boosting (XGB) Initially, the data were preprocessed using several preprocessing techniques Furthermore, 10-k cross-validation was applied for data partitioning and SMOTE for alleviating the data imbalance Experiments were performed using twenty clinical features, identified as significant for predicting the survival versus the deceased COVID-19 patients The results showed that RF outperformed the other classifiers with an accuracy of 0 95 and area under curve (AUC) of 0 99 The proposed model can assist the decision-making and health care professional by early identification of at-risk COVID-19 patients effectively [ABSTRACT FROM AUTHOR] Copyright of Scientific Programming is the property of Hindawi Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission However, users may print, download, or email articles for individual use This abstract may be abridged No warranty is given about the accuracy of the copy Users should refer to the original published version of the material for the full abstract (Copyright applies to all Abstracts )