Relative Fat Mass and Physical Indices as Predictors of Gallstone Formation: Insights From Machine Learning and Logistic Regression.

Purpose: Gallstones (GS), a prevalent disorder of the biliary tract, markedly impair patients' quality of life. This study aims to construct predictive models employing diverse machine learning algorithms to elucidate risk factors linked to gallstone formation. Patients and Methods: This study integrated data from the National Health and Nutrition Examination Survey (NHANES) with a cohort of 7868 participants from Wuxi People's Hospital and Wuxi Second People's Hospital, including 830 individuals diagnosed with gallstones. To develop our predictive model, we employed four algorithms-Logistic Regression, Gaussian Naive Bayes (GNB), Multi-Layer Perceptron (MLP), and Support Vector Machine (SVM). The models were validated internally through k-fold cross-validation and externally using independent datasets. Furthermore, we substantiated the link between relative fat mass (RFM) and gallstone formation by employing four logistic regression models, conducting subgroup analyses, and applying restricted cubic spline (RCS) curves. Results: The logistic regression algorithm demonstrated superior predictive capability for all risk factors associated with gallstone occurrence compared to other machine learning models. SHAP analysis identified RFM, weight-to-waist index (WWI), waist circumference (WC), waist-to-height ratio (WHtR), and body mass index (BMI) as prominent predictors of gallstone occurrence, with RFM emerging as the primary determinant. A fully adjusted multivariate logistic regression analysis revealed a robust positive association between RFM and gallstones. Subgroup analysis further indicated that subgroup factors did not alter the positive relationship between RFM and gallstone prevalence. Conclusion: Among the four algorithmic models, logistic regression proved most effective in predicting gallstone occurrence. The model developed in this study offers clinicians a valuable tool for identifying critical prognostic factors, facilitating personalized patient monitoring and tailored management.