Determination of malignancy risk factors using gallstone data and comparing machine learning methods to predict malignancy

Cetin, S, Ulgen, A ORCID: https://orcid.org/0000-0002-0872-667X, Pasin, O, Sivgin, H and Cetin, M, 2025. Determination of malignancy risk factors using gallstone data and comparing machine learning methods to predict malignancy. Journal of Clinical Medicine, 14 (17): 6091. ISSN 2077-0383

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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.

Item Type:	Journal article
Publication Title:	Journal of Clinical Medicine
Creators:	Cetin, S., Ulgen, A., Pasin, O., Sivgin, H. and Cetin, M.
Publisher:	MDPI AG
Date:	1 September 2025
Volume:	14
Number:	17
ISSN:	2077-0383
Identifiers:	Number Type 10.3390/jcm14176091 DOI 2519446 Other
Rights:	© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Divisions:	Schools > School of Science and Technology
Record created by:	Laura Borcherds
Date Added:	28 Oct 2025 18:00
Last Modified:	28 Oct 2025 18:00
URI:	https://irep.ntu.ac.uk/id/eprint/54645

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