Abstract
Background/Objectives: Gallstone disease, a prevalent and costly digestive system disorder, is influenced by multifactorial risk factors, some of which may predispose to malignancy. This study aims to evaluate the association between gallstone disease and malignancy using advanced machine learning (ML) algorithms. Methods: A dataset comprising approximately 1000 patients was analyzed, employing six ML methods: random forests (RFs), support vector machines (SVMs), multi-layer perceptron (MLP), MLP with PyTorch 2.3.1 (MLP_PT), naive Bayes (NB), and Tabular Prior-data Fitted Network (TabPFN). Comparative performance was assessed using Pearson correlation, sensitivity, specificity, Kappa, receiver operating characteristic (ROC), area under curve (AUC), and accuracy metrics. Results: Our results revealed that age, body mass index (BMI), and history of HRT were the most significant predictors of malignancy. Among the ML models, TabPFN emerged as the most effective, achieving superior performance across multiple evaluation criteria. Conclusions: This study highlights the potential of leveraging cutting-edge ML methodologies to uncover complex relationships in clinical datasets, offering a novel perspective on gallstone-related malignancy. By identifying critical risk factors and demonstrating the efficacy of TabPFN, this research provides actionable insights for predictive modeling and personalized patient management in clinical practice.