Web-based Machine Learning Model for Predicting Chronic Kidney Disease in Patients with Type 2 Diabetes Mellitus: A Multicenter Study.

Background: Chronic kidney disease (CKD) is a serious complication of type 2 diabetes (T2DM), particularly in low- and middle-income countries with limited access to early diagnosis. Predicting CKD risk using routine clinical data could enable earlier nephroprotective care. This study developed and internally validated a machine learning-based web application to predict incident CKD among T2DM patients in Indonesia's national health insurance program (Prolanis). Methods: A machine learning prediction model was conducted using BPJS Prolanis data (2017-2023). Adults (≥18 years) with T2DM and no prior CKD were included. Six algorithms (Logistic Regression, Random Forest, Decision Tree, XGBoost, LightGBM, CatBoost) were trained on 80% of the data and internally validated on the remaining 20% to predict CKD. Performance was assessed via accuracy, precision, recall, F1 score, and AUC. SHAP was used for interpretability. Results: Among 7,581 individuals, 864 (11.4%) developed CKD. CatBoost achieved the best performance (AUC = 0.847, accuracy = 0.797, precision = 0.643, recall = 0.525, F1 = 0.578). SHAP identified rapid-acting insulin analogues, amlodipine, furosemide, high blood urea nitrogen, and folic acid as key positive predictors. Advanced age and higher comorbidity burden increased risk, while chronic ischaemic heart disease and dental pulp diseases appeared protective-likely due to healthcare utilization bias. A web-based risk calculator was developed. Conclusions: The CatBoost-based web app demonstrated strong discriminative ability for predicting incident CKD in T2DM patients using routine claims data. This tool may support risk stratification in primary care settings across Indonesia and similar low-resource environments.