Bayesian Hybrid Machine Learning of Gallstone Risk

TL;DR

This paper introduces a hybrid Bayesian machine learning approach combining variable selection, interaction detection, and physiological knowledge to improve gallstone risk prediction and interpretability.

Contribution

It develops a novel framework integrating Adaptive LASSO, BART, and Bayesian logistic regression with physiological insights for better risk modeling.

Findings

Enhanced prediction accuracy over traditional methods

Identification of key nonlinear interactions and features

Model grounded in biological plausibility

Abstract

Gallstone disease is a complex, multifactorial condition with significant global health burdens. Identifying underlying risk factors and their interactions is crucial for early diagnosis, targeted prevention, and effective clinical management. Although logistic regression remains a standard tool for assessing associations between predictors and gallstone status, it often underperforms in high-dimensional settings and may fail to capture intricate relationships among variables. To address these limitations, we propose a hybrid machine learning framework that integrates robust variable selection with advanced interaction detection. Specifically, Adaptive LASSO is employed to identify a sparse and interpretable subset of influential features, followed by Bayesian Additive Regression Trees (BART) to model nonlinear effects and uncover key interactions. Selected interactions are further characterized by physiological knowledge through differential equation-informed interaction terms, grounding the model in biologically plausible mechanisms. The insights gained from these steps are then integrated into a final logistic regression model within a Bayesian framework, providing a balance between predictive accuracy and clinical interpretability. This proposed framework not only enhances prediction but also yields actionable insights, offering a valuable support tool for medical research and decision-making.