A Risk-Oriented and Explainable Hierarchical AI Framework for Chronic Kidney Disease Classification

Background/Objectives: Chronic kidney disease (CKD) remains a major public health challenge due to its silent progression and late clinical detection. Recent advances in machine learning have demonstrated promising performance in CKD detection; however, most existing approaches focus primarily on binary classification or rely on longitudinal or specialized biomarkers that are not routinely available in clinical practice. While several studies attempt risk stratification, few integrate risk modeling with stage-aware hierarchical decision frameworks suitable for routine clinical workflows. This study proposes a risk-oriented, explainable, and hierarchical machine learning framework for CKD classification using real-world laboratory data from 746 patients in a Saudi population. Methods: The proposed framework is designed as a hierarchical machine learning pipeline that mirrors clinical practice by sequentially identifying CKD presence, performing disease staging only for confirmed cases, and estimating risk for individuals without overt CKD. Specifically, an XGBoost model with recursive feature elimination (RFE) was employed for binary CKD detection, followed by a multilayer perceptron (MLP) model with SelectKBest for stage classification. A unified preprocessing pipeline, clinically informed feature selection, and validated machine learning models were employed to develop the hierarchical prediction system. Results: The system achieved 97% accuracy and F1-score in binary CKD classification, and up to 85% accuracy and 86% F1-score in stage classification. In addition, an interpretable risk scoring mechanism and SHAP-based explanations enabled early identification of CKD-like laboratory patterns using routine laboratory tests. Conclusions: The proposed system provides a transparent and deployable framework that supports preventive nephrology and clinically meaningful decision-making.