EARLY PREDICTION OF CHRONIC KIDNEY DISEASE USING AN ENSEMBLE MACHINE LEARNING-BASED CLINICAL DECISION SUPPORT SYSTEM - Anitha, Kancharla; Rao, B. Basaveswara (ShodhKosh: Journal of Visual and Performing Arts, 2026)
Chronic Kidney Disease (CKD) is a progressive medical condition that requires early prediction to reduce complications and healthcare costs. This study presents an ensemble-based clinical decision support system for early CKD prediction using two public datasets: UCI-CKD and CKD-15. A structured preprocessing pipeline was implemented. A fine-tuned Random Forest classifier was employed, and a feature ablation study was conducted to analyze the contribution of numerical, categorical, and clinically selected feature groups. Model performance was evaluated using stratified k-fold cross-validation (3-, 5-, and 10-fold). The model achieved near-perfect performance on the UCI-CKD dataset with 99.99% accuracy under cross-validation. On the CKD-15 dataset, the model achieved a maximum accuracy of 92.41%, with numerical features providing the highest predictive performance. The results demonstrate strong classification capability and good generalization for early CKD prediction.