Explainable AI and Machine Learning for Chronic Kidney Disease Detection: Integrating Clinical Decision Support, Telemedicine, and Personalized Healthcare Management

Abstract

Chronic kidney disease impacts millions worldwide, and delayed diagnosis results in unfavorable outcomes and higher healthcare expenses. Recent advancements in machine learning present promising diagnostic features, but their “black box” nature restricts clinical uptake. This review surveys recent methods for CKD detection, emphasizing the urgent need to bridge the gap between predictive performance and clinical interpretability. We examine traditional machine learning models, deep learning algorithms, and emerging explainable AI approaches. The work synthesizes research on CKD prediction, telemedicine integration, and donor-matching platforms. Our analysis demonstrates that although many high-accuracy models exist, few provide transparent decision-making explanations essential for clinicians. We propose an integrated approach involving interpretable decision tree models and comprehensive patient-management features such as remote consultations and personalized lifestyle recommendations. This paradigm meets the dual challenge of balancing diagnostic accuracy with clinical transparency, potentially transforming early CKD detection and long-term disease management.