Machine learning-based early kidney stone risk classification in diabetic-hypertensive patients using obesity composite indices

Kidney stone disease (KSD) is increasingly prevalent among patients with diabetes mellitus and hypertension. Obesity-related metabolic abnormalities may be associated with stone formation, yet their combined association with KSD has not been fully explored. Using data from the National Health and Nutrition Examination Survey (NHANES) 2007-2018, we conducted a cross-sectional study including adults with both diabetes and hypertension. Eight obesity-related composite indices were used as predictors, and self-reported KSD history was defined as the outcome. Nine supervised machine learning algorithms were developed and compared using cross-validation within the training set. Model hyperparameters were tuned using cross-validation within the training set, with the area under the receiver operating characteristic curve (AUC) specified a priori as the primary performance metric. Final model performance was evaluated on an independent test set. To interpret the model, we employed the SHapley Additive exPlanations (SHAP) method, which quantifies both the importance and marginal association of each feature. Subsequently, the final selected model was interpreted using SHAP and deployed as an interactive web application using Shiny. Among the 9 models, the Random Forest classifier demonstrated the best discriminative performance based on cross-validated training performance and achieved an AUC of 0.895 (0.864-0.926) in the independent test set, along with acceptable calibration. Feature importance and SHAP analyses consistently identified the roundness fat mass (RFM) and lipid accumulation product (LAP) as the features most strongly associated with KSD. Obesity-related composite indices were significantly associated with KSD in diabetic-hypertensive adults. The Random Forest model showed superior and discriminative performance among the evaluated algorithms, supporting its potential utility in risk stratification. An interactive web-based Shiny app was developed to enhance clinical applicability: https://obesityrelated.shinyapps.io/apps2/.