Interpretable machine learning for gallstone risk prediction: an ensemble stacking approach with SHAP analysis

Gallstone disease is a gastrointestinal condition requiring accessible, low-cost screening tools. We developed an interpretable ensemble stacking model for noninvasive gallstone prediction using bioimpedance/laboratory data from 319 individuals (157 patients, 162 controls) with 38 features. Bayesian-optimized SVM, logistic regression, and decision tree models were combined with a deep neural network meta-learner. Ten repeated nested cross-validation yielded 82.3% accuracy and 85.7% AUC, outperforming classical stacking and eight algorithms. SHAP identified CRP, liver enzymes, and HDL cholesterol as key, clinically consistent contributors. However, substantial multicollinearity (VIF >100) caused directional instability for vitamin D and diabetes. Prospective multicenter validation remains necessary before implementation.