Comparative machine learning to predict acute kidney injury in traumatic brain injury: a MIMIC-IV cohort with SHAP interpretation

Ensemble ML models, particularly XGBoost, demonstrated robust predictive power, outperforming LR and DT. The XGBoost model combined high discrimination, calibration, and interpretability, offering a clinically applicable tool for early AKI risk stratification in TBI.