Sustainable Healthcare With Explainable and Energy‐Efficient Ensemble Machine Learning for Chronic Kidney Disease Prediction

ABSTRACT Chronic Kidney Disease (CKD) is a growing global health burden whose late detection leads to costly treatments and long‐term strain on healthcare systems. Early prediction supported by Artificial Intelligence (AI) can contribute to more sustainable healthcare by reducing resource‐intensive interventions such as dialysis and transplantation. This study presents an efficiency‐aware and explainable machine learning framework for CKD prediction using the UCI CKD dataset. Thirteen models including Logistic Regression, SVM, KNN, Decision Tree, Random Forest, XGBoost, LightGBM and CatBoost are evaluated using accuracy, precision, recall, F1‐score, ROC‐AUC, calibration, learning curves and confusion matrices. To align with sustainable and green AI principles, we additionally analyse model complexity and execution time as proxies for computational cost. Explainable AI techniques (SHAP and LIME) are integrated to ensure transparency and support trustworthy clinical deployment. Results show that ensemble models, especially LightGBM, XGBoost and CatBoost, achieve superior predictive performance, whereas LR and LightGBM offer strong trade‐offs between accuracy, interpretability and efficiency. The experimental findings on the UCI CKD dataset indicate that ensemble models attain a performance of up to 100% accuracy, with various classifiers having an accuracy of over 98% with their calibration, interpretability and computational efficiency being high. The proposed framework demonstrates how explainable and energy‐efficient AI can enhance early CKD detection and support sustainable, resource‐conscious healthcare systems.