Chronic Kidney Disease Prediction System Using Machine Learning

Abstract: Chronic kidney disease (CKD) is a progressive condition that often remains unnoticed until a large part of kidney function has been lost, which makes early screening one of the most effective ways of delaying dialysis and transplantation. Machine learning (ML) can learn the relationship between routine clinical and laboratory measurements and the presence of CKD, and can turn it into a fast, low-cost decision-support tool. This paper presents a structured

and ensemble classifiers, feature selection, class-imbalance handling, explainable artificial intelligence and progression prediction. The studies are compared in a summary table and the common gaps are identified: reliance on small public datasets, near-perfect but weakly validated accuracy, limited external validation, explanations that never reach the user, and few complete systems that connect prediction to patient records and reports. Based on these gaps, the motivation, application areas and future directions for a practical web-based CKD prediction system built on a Random Forest classifier are discussed.

Keywords: Chronic kidney disease, Machine learning, Randomforest, Early prediction, Explainable AI, Feature selection, Clinical decision support.