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Chronic Kidney Disease (CKD) represents a significant global health challenge, particularly relevant to Morocco where renal diseases impose a heavy burden on the healthcare system. This study presents an interpretable machine learning framework for CKD prediction using XGBoost models enhanced with SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME). Evaluated on a dataset from Tawam Hospital in Abu Dhabi comprising 491 patients with comprehensive demographic, clinical, and biochemical data, the XGBoost model demonstrated exceptional performance. Key predictive biomarkers identified include eGFRBaseline, CholesterolBaseline, and AgeBaseline. These findings not only validate the biological plausibility of the model but also demonstrate its ability to provide accurate, interpretable predictions that can significantly impact healthcare in Morocco. The integration of SHAP and LIME offers clinicians transparent insights into model decisions, enhancing trust and facilitating clinical adoption. This work highlights the transformative potential of interpretable machine learning in nephrology, paving the way for improved patient outcomes through enhanced early detection, risk stratification, and personalized treatment strategies in the Moroccan context.

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Interpretable Machine Learning for Chronic Kidney Disease Prediction Using Clinical Data

  • 会议论文

  • 发表日期：

    2026年

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  • El Mehdi Chouit,
  • Mohamed Rachdi,
  • Mostafa Bellafkih,
  • Brahim Raouyane

摘要

Chronic Kidney Disease (CKD) represents a significant global health challenge, particularly relevant to Morocco where renal diseases impose a heavy burden on the healthcare system. This study presents an interpretable machine learning framework for CKD prediction using XGBoost models enhanced with SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME). Evaluated on a dataset from Tawam Hospital in Abu Dhabi comprising 491 patients with comprehensive demographic, clinical, and biochemical data, the XGBoost model demonstrated exceptional performance. Key predictive biomarkers identified include eGFRBaseline, CholesterolBaseline, and AgeBaseline. These findings not only validate the biological plausibility of the model but also demonstrate its ability to provide accurate, interpretable predictions that can significantly impact healthcare in Morocco. The integration of SHAP and LIME offers clinicians transparent insights into model decisions, enhancing trust and facilitating clinical adoption. This work highlights the transformative potential of interpretable machine learning in nephrology, paving the way for improved patient outcomes through enhanced early detection, risk stratification, and personalized treatment strategies in the Moroccan context.