Bayesian Optimization Meets Explainable AI: Enhanced Chronic Kidney Disease Risk Assessment

Jianbo Huang, Long Li, Mengdi Hou and Jia Chen ()
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Jianbo Huang: School of Computer Application, Guilin University of Technology, Guilin 541004, China
Long Li: Key Laboratory of Equipment Data Security and Guarantee Technology, Ministry of Education, Guilin University of Electronic Technology, Guilin 541004, China
Mengdi Hou: School of Electronic Information and Artificial Intelligence, Wuzhou University, Wuzhou 543000, China
Jia Chen: School of Computer Application, Guilin University of Technology, Guilin 541004, China

Mathematics, 2025, vol. 13, issue 17, 1-35

Abstract: Chronic kidney disease (CKD) affects over 850 million individuals worldwide, yet conventional risk stratification approaches fail to capture complex disease progression patterns. Current machine learning approaches suffer from inefficient parameter optimization and limited clinical interpretability. We developed an integrated framework combining advanced Bayesian optimization with explainable artificial intelligence for enhanced CKD risk assessment. Our approach employs XGBoost ensemble learning with intelligent parameter optimization through Optuna (a Bayesian optimization framework) and comprehensive interpretability analysis using SHAP (SHapley Additive exPlanations) to explain model predictions. To address algorithmic “black-box” limitations and enhance clinical trustworthiness, we implemented four-tier risk stratification using stratified cross-validation and balanced evaluation metrics that ensure equitable performance across all patient risk categories, preventing bias toward common cases while maintaining sensitivity for high-risk patients. The optimized model achieved exceptional performance with 92.4% accuracy, 91.9% F1-score, and 97.7% ROC-AUC, significantly outperforming 16 baseline algorithms by 7.9–18.9%. Bayesian optimization reduced computational time by 74% compared to traditional grid search while maintaining robust generalization. Model interpretability analysis identified CKD stage, albumin-creatinine ratio, and estimated glomerular filtration rate as primary predictors, fully aligning with established clinical guidelines. This framework delivers superior predictive accuracy while providing transparent, clinically-meaningful explanations for CKD risk stratification, addressing critical challenges in medical AI deployment: computational efficiency, algorithmic transparency, and equitable performance across diverse patient populations.

Keywords: chronic kidney disease; Bayesian optimization; explainable AI; risk assessment; clinical decision support (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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