PREDICTION OF CHRONIC KIDNEY DISEASE USING MACHINE LEARNING ALGORITHMS

Abstract

Chronic Kidney Disease (CKD) is a progressive condition that affects millions worldwide and poses a major public 
health challenge due to its high morbidity and mortality rates. Early detection and accurate prediction are critical for 
effective intervention and improved patient outcomes. In recent years, machine learning (ML) algorithms have 
emerged as powerful tools for CKD prediction, offering enhanced diagnostic accuracy compared to traditional 
statistical methods. This review synthesizes current research on ML-based CKD prediction, highlighting commonly 
used datasets, feature selection techniques, and algorithms such as decision trees, support vector machines, random 
forests, logistic regression, and deep learning models. Studies demonstrate that ML approaches can effectively 
identify key risk factors including blood pressure, serum creatinine, and glucose levels while achieving high 
predictive performance across diverse populations. However, challenges remain in terms of data imbalance, model 
interpretability, and generalizability across clinical settings. Future directions emphasize the integration of 
explainable AI, federated learning, and multimodal data sources to improve transparency and robustness. Overall, 
ML-driven CKD prediction holds significant promise for advancing precision medicine and supporting early clinical 
decision-making.