An Intelligent Decision-Support Framework for AST Risk Prediction Using Explainable Ensemble Learning

This paper proposes an intelligent and explainable ensemble system for predicting as-partate aminotransferase (AST) levels based on routine biochemical and demographic data from the NHANES dataset. The framework integrates robust preprocessing, adaptive feature encoding, and multi-level ensemble learning within a nested cross-validation (5×3) structure to ensure reproducibility and prevent data leakage. Several regression mod-els—including Random Forest, XGBoost, CatBoost, and stacking ensembles—were sys-tematically compared using R², RMSE, MAE, and MAPE metrics. The results show that the Stacking v2 architecture, combining CatBoost, LightGBM, and Ridge meta-regression, achieves the highest predictive accuracy and stability. Explainable AI analysis using SHAP revealed key biochemical and lifestyle factors influencing AST variability. The pro-posed system provides a modular, interpretable, and reproducible foundation for deci-sion-support applications in intelligent healthcare analytics, aligning with the goals of applied system innovation.