Robust Machine Learning Framework for Early Risk Prediction of Chronic Kidney Disease Using Multidimensional Clinical Data

Abstract

Chronic Kidney Disease (CKD) is a progressive and often asymptomatic condition that places a significant global bur¬den on healthcare systems due to late diagnosis and irreversible kidney damage. Identifying high-risk individuals early remains difficult in routine clinical practice because of diverse patient profiles, incomplete medical records, and complex, nonlinear interactions among clinical factors. Traditional statistical and rule-based screening methods typically fail to account for these complexities, reducing their reliability in real-world scenarios. This study introduces a clinically focused machine learning framework for early CKD risk prediction using multidimensional clinical data. The framework incorporates systematic data pre-processing, feature relevance analysis, imbalance-aware learning, and ensemble-based modeling to capture intricate dependencies among demographic, biochemical, and physiological factors. Gradient-boosted decision trees are used as the primary predic¬tive model due to their ability to handle nonlinear relationships and missing data commonly found in clinical datasets. The model emphasizes probability calibration, threshold-sensitive evalua¬tion, and clinical feasibility over accuracy alone. Experimental results show that the optimized and calibrated gradient boosting model provides better discriminative performance and reliable probability estimates. Decision curve analysis and threshold trade-off evaluation confirm consistent clinical net benefit across relevant screening thresholds. Ensemble-based strategies further improve prediction stability across different patient subgroups. These results show that the suggested framework can help with early CKD screening by focusing on detecting cases accurately and reducing the chances of missing diagnoses. Even though the study was based on one dataset and used past data, it shows that machine learning models that are well-calibrated and aware of how decisions are made can help doctors manage CKD risks more effectively