A multiclass machine learning framework for chronic kidney disease staging using CTGAN-based synthetic data
1Department of Computer Science and Engineering, National Institute of Technology Nagaland, Chumukedima, Nagaland, India.
Computer Methods in Biomechanics and Biomedical Engineering
|January 12, 2026
Summary
This study developed an AI framework for predicting Chronic Kidney Disease (CKD) stages using eGFR. The Random Forest model achieved 97.92% accuracy, offering reliable and explainable CKD staging.
Area of Science:
- Nephrology
- Artificial Intelligence
- Machine Learning
Background:
- Chronic Kidney Disease (CKD) staging is crucial for intervention.
- Existing studies often use binary classification, limiting granular prediction.
- Accurate staging requires robust predictive models.
Purpose of the Study:
- To develop an AI-driven multiclass machine learning framework for CKD staging.
- To utilize estimated glomerular filtration rate (eGFR) for staging prediction.
- To enhance model performance and interpretability in CKD prediction.
Main Methods:
- Utilized a clinically validated UCI dataset for CKD staging.
- Employed CTGAN for data augmentation to address imbalance and scarcity.
- Evaluated Random Forest, XGBoost, and Multi-Layer Perceptron models via 10-fold cross-validation.
- Applied SHAP for model interpretability.
Main Results:
- Random Forest model achieved the highest accuracy at 97.92%.
- The framework successfully performed multiclass CKD staging.
- SHAP analysis identified key biomarkers for CKD prediction.
Conclusions:
- AI-driven multiclass ML framework enables accurate CKD staging.
- Random Forest demonstrates superior performance for CKD stage prediction.
- Explainable AI provides insights into clinically relevant biomarkers for CKD.
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