What question did this study set out to answer?

The aim is to explore machine learning techniques for classifying stages of chronic kidney disease based on clinical data.

April 15, 2026Open Access

Machine Learning-Based Multi-Class Classification of Chronic Kidney Disease Stages Using XGBoost: A Feature-Driven Predictive Modelling Approach

Key Points

The aim is to explore machine learning techniques for classifying stages of chronic kidney disease based on clinical data.
Implemented machine learning algorithms, primarily XGBoost, for classification.
Utilized features from routinely collected clinical variables.
Compared the effectiveness of machine learning classification against conventional methods.
The machine learning model demonstrated high accuracy in classifying CKD stages.
XGBoost outperformed traditional methods in predicting disease progression.

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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Authors

Saarthak Dubey

Revanth Kumar

Ramarao

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Machine Learning-Based Multi-Class Classification of Chronic Kidney Disease Stages Using XGBoost: A Feature-Driven Predictive Modelling Approach

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Abstract

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Discussion

Authors

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