Using Explainable Machine Learning for Early Detection of Diabetic Kidney Disease in Rwandan Diabetic Patients

Abstract 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. This study employs explainable artificial intelligence (XAI) to predict DKD risk in diabetic patients using tree-based ensemble models and SHAP (Shapley Additive exPlanations), leveraging the MIMIC-IV dataset and a dataset from hospitals in Rwanda. Among the models used, Random Forest demonstrated superior performance, achieving accuracies of 87.97% on MIMIC-IV and 91.70% on the Rwandan 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. Our findings highlight the promising potential of integrating predictive modeling with explainability to develop transparent and trustworthy tools for early detection of DKD, with potential applications in clinical workflows.