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Submitted
August 12, 2025
Accepted
October 20, 2025
Published
October 29, 2025
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Abstract

Gallstone disease (cholelithiasis) is a digestive system disorder with a globally increasing prevalence. This study aims to identify risk factors contributing to the occurrence of gallstones using a logistic regression model. The data were obtained from the UC Irvine Machine Learning Repository, comprising a total of 319 outpatients from Ankara VM Medical Park Hospital, Turkey. The analysis was conducted on 23 independent variables, including demographic characteristics, body composition, medical history, and laboratory results. The Chi-Square test identified four significant variables, while the Wald test revealed six statistically significant predictors of gallstone occurrence: age, comorbidities, diabetes mellitus, visceral fat rating, visceral fat area, and vitamin D levels. Diabetes mellitus emerged as the most dominant risk factor (OR = 11.5), whereas higher levels of vitamin D showed a protective effect. The logistic regression model demonstrated a classification accuracy of 77%, indicating good predictive performance. These findings are expected to support early detection, clinical decision-making, and preventive interventions for more effective gallstone prevention.

Keywords

Gallstone Binary logistic regression Risk Factors Clinical Prediction

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Copyright (c) 2025 Ihsan Fathoni Amri, Febrian Hikmah Nur Rohim, M. Ilham Nurul Azka, Muji Silvi Rakhmawati

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How to Cite
Amri, I. F., Rohim, F. H. N. ., Nurul Azka, M. I., & Rakhmawati, M. S. . (2025). The Analysis of Gallstone Incidence Factors Using a Logistic Regression Model. EKSAKTA: Journal of Sciences and Data Analysis, 6(2). https://doi.org/10.20885/EKSAKTA.vol6.iss2.art3
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