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Machine Learning-Based Multi-Class Classification of Chronic Kidney Disease Stages Using XGBoost: A Feature-Driven Predictive Modelling Approach
Abstract
Chronic kidney disease (CKD) is a major non-communicable disease burden affecting approximately 10–15% of the global adult population. Accurate and early classification of CKD progression stages is critical for timely clinical intervention and improved patient outcomes. Conventional staging relies on laboratory estimation of glomerular filtration rate (eGFR); however, machine learning methods offer an opportunity to classify multi-stage CKD from routinely collected clinical variables with high accuracy.
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