A Systematic Review of Machine Learning Methods for Chronic Kidney Disease Diagnosis and Prediction (2020-2025)

This paper reviews the use of machine learning techniques (ML) in diagnosing and predicting Chronic Kidney Disease (CKD) in articles published between 2020 and 2025. Across twenty selected studies, we systematically analyzed data preprocessing techniques, feature selection methods, classification algorithms, and performance evaluation metrics. We conclude that there is a significant trend towards ensemble and hybrid models, which outperform traditional single algorithms. At the same time, deep learning still shows remarkable predictive results with limited interpretability challenges. Feature selection remains one of the few constants for improving accuracy, efficiency, and transparency. Major challenges include limited datasets, class imbalance, and the gap between the proposed models and clinical implementation. Our review highlights the need for standardized evaluation methods, diverse multicenter datasets, and increased concentration on explainable AI to ease and enhance clinical integration of machine learning based CKD diagnostic tools.