An Explainable Ensemble Machine-Learning Framework for Predicting ICU Mortality in Critically Ill Patients

1. A 71-feature clinical feature-engineering pipeline built from 22 raw ICU variables.
2. An ensemble architecture combining XGBoost,LightGBM, CatBoost, HistGradientBoosting,ExtraTrees, and Random Forest.
3. An optimized weighted ensemble with logisticregressionstacking to fuse predictions from complementary base learners.
4. A preprocessing and class-balancing pipeline usingmedian imputation, SMOTE,RobustScaler, and F1-driven threshold tuning.