AN OPTIMIZED XGBOOST-BASED FRAMEWORK FOR HEART FAILURE RISK CLASSIFICATION
A machine learning framework for heart failure survival prediction that makes use of an optimized XGBoost model combined with the Synthetic Minority Over-sampling Technique (SMOTE) has the potential to enhance patient outcomes through accurate and prompt clinical decision-making.