Gradient boosting models for gallstone disease classification: A comparative study using demographic and clinical data

Gallstone disease is a prevalent gastrointestinal condition with a substantial healthcare burden. While abdominal ultrasonography is the established first-line diagnostic modality, opportunistic risk stratification using routinely collected health parameters remains underexplored. This study evaluated the discriminatory capacity of four gradient boosting machine learning algorithms (XGBoost, LightGBM, CatBoost, and HistGBM) for classifying gallstone status using demographic, bioimpedance, and routine laboratory features. A retrospective clinical dataset of 319 patients (161 gallstone-positive individuals and 158 healthy controls) from Ankara VM Medical Park Hospital was analyzed. Model performance was assessed using repeated stratified 10-fold cross-validation, evaluated via AUC-ROC, accuracy, F1-score, sensitivity, specificity, Brier score, and decision curve analysis (DCA), with SHAP analyses employed for model interpretability. CatBoost achieved the highest discriminatory performance (AUC = 0.862±0.061, accuracy = 0.794±0.075) and the lowest Brier score (0.149±0.037), demonstrating statistically significant superiority over competing architectures (Friedman test, p