Prediction of COVID-19 Victim's Well-being using eXtreme Gradient Boost Algorithm

In many areas, Machine Learning (ML) and data-driven approaches are becoming very important and popular. Gradient tree boosting is one technique of the machine learning approaches included in practice that appears in many application domains. Tree boosting has appeared to execute numerous normal measurements for classified benchmarks. A highly efficient and commonly used machine learning approach is tree boosting. This study proposed a Calibrated Random Forest Model (CRFM) improved by the eXtreme Gradient (XG) Boost algorithm. The model takes into account the location, method of transportation, medical history, and demographics of the Corona Virus Disease-2019 (COVID-19) patient to predict the severity of the case and the likelihood of a positive or negative outcome. The system has a 0.92 F1 score and 95% accuracy on the repository used. The descriptive statistics indicate a strong link between sexual orientation and demise and also show that most victims are between the ages of 40 and 60 in India.