Predicting the Prognosis of Patients in the Coronary Care Unit via Machine Learning Using XGBoost

Background: Early prediction and classification of prognosis is essential for patients in the coronary care unit (CCU). We applied a machine learning (ML) model using the XGBoost algorithm to prognosticate CCU patients, and compared XGBoost with traditional classification models. Methods: CCU patients’ data were extracted from the MIMIC-III v1.4 clinical database, and divided into four groups based on the time to death: <30 days, 30 days–1...