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 < 0.001). Feature ablation revealed that bioimpedance parameters alone provided substantial independent discriminative signal (AUC = 0.797±0.079), approaching that of laboratory markers (AUC = 0.829±0.070), whereas demographic attributes alone were insufficient (AUC = 0.476±0.097). SHAP analyses identified C-reactive protein, Vitamin D, and bone mass as the most influential variables, alongside non-linear risk associations for visceral fat area and body mass index. Decision curve analysis demonstrated positive net clinical benefit across threshold probabilities of 0.10-0.80. These findings demonstrate that gradient boosting models, particularly CatBoost, can provide robust, interpretable retrospective classification of gallstone disease, offering proof-of-concept potential for opportunistic risk stratification in metabolic care settings and pre-imaging triage for ambiguous outpatient presentations rather than replacing first-line ultrasonography.