Using explainable machine learning for early detection of diabetic kidney disease in Rwandan diabetic patients

Abstract Background The global prevalence of diabetes is increasing, often leading to complications such as diabetic kidney disease (DKD). Uncontrolled blood glucose and hypertension are key risk factors that progressively impair kidney function, potentially resulting in kidney failure or end-stage renal disease (ESRD). Early detection of DKD is crucial but challenging due to its asymptomatic onset. Methods This study employs explainable artificial intelligence (XAI) to predict DKD risk in diabetic patients using tree-based ensemble models and Shapley Additive exPlanations (SHAP), leveraging the Medical Information Mart for Intensive Care (MIMIC)-IV dataset and a dataset from hospitals in Rwanda. Results Among the models used, Random Forest demonstrated superior performance, achieving accuracies of 87.97% on MIMIC-IV and 91.70% on the Rwandan dataset. XGBoost outperformed other models, achieving 79.32% accuracy when trained on the Rwanda dataset and evaluated on the MIMIC-IV dataset. Models were evaluated using multiple metrics, including ROC-AUC and calibration curves. SHAP provided both global and individual-level explanations, with predictions validated using estimated glomerular filtration rate (eGFR) values. Conclusion Our findings highlight the potential of combining predictive modeling with explainability to support transparent and trustworthy early detection of DKD in clinical practice. Compared with the current eGFR-based risk assessment, this approach incorporates additional predictors that improve risk identification. Blood urea nitrogen (Urea), Hemoglobin, Total cholesterol (TC), Blood glucose (BG), and Creatinine are the leading predictors of DKD risk. This study suggests that the XAI model can be used to assess DKD risk, while eGFR remains valuable for confirming the diagnosis and disease stage.