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

Laifu Deng,1,* Shuting Wang,1,* Daiwei Wan,1,* Qi Zhang,2 Wei Shen,1 Xiao Liu,1,* Yu Zhang1,* 1Department of General Surgery, Wuxi Medical Center of Nanjing Medical University, Wuxi, People’s Republic of China; 2Department of Oncology, Tengzhou Central People’...

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Main Authors: Deng L, Wang S, Wan D, Zhang Q, Shen W, Liu X, Zhang Y
Format: Article
Language:English
Published: Dove Medical Press 2025-01-01
Series:International Journal of General Medicine
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Online Access:https://www.dovepress.com/relative-fat-mass-and-physical-indices-as-predictors-of-gallstone-form-peer-reviewed-fulltext-article-IJGM
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author Deng L
Wang S
Wan D
Zhang Q
Shen W
Liu X
Zhang Y
author_facet Deng L
Wang S
Wan D
Zhang Q
Shen W
Liu X
Zhang Y
author_sort Deng L
collection DOAJ
description Laifu Deng,1,* Shuting Wang,1,* Daiwei Wan,1,* Qi Zhang,2 Wei Shen,1 Xiao Liu,1,* Yu Zhang1,* 1Department of General Surgery, Wuxi Medical Center of Nanjing Medical University, Wuxi, People’s Republic of China; 2Department of Oncology, Tengzhou Central People’s Hospital, Jining Medical College, Shandong, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xiao Liu; Yu Zhang, Department of General Surgery, Wuxi Medical Center of Nanjing Medical University, Wuxi, People’s Republic of China, Tel +8613561164619 ; +8613793706777, Email ningzhouwxrmyy@163.com; yuzhangjs@yeah.netPurpose: 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.Keywords: gallstones, atherogenic index of plasma, national health and nutrition examination survey, cross-sectional studies, risk factor, machine learning
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spelling doaj-art-1b22271220b14a2493a4ac412c3b19fc2025-02-02T15:59:40ZengDove Medical PressInternational Journal of General Medicine1178-70742025-01-01Volume 1850952799762Relative Fat Mass and Physical Indices as Predictors of Gallstone Formation: Insights From Machine Learning and Logistic RegressionDeng LWang SWan DZhang QShen WLiu XZhang YLaifu Deng,1,* Shuting Wang,1,* Daiwei Wan,1,* Qi Zhang,2 Wei Shen,1 Xiao Liu,1,* Yu Zhang1,* 1Department of General Surgery, Wuxi Medical Center of Nanjing Medical University, Wuxi, People’s Republic of China; 2Department of Oncology, Tengzhou Central People’s Hospital, Jining Medical College, Shandong, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xiao Liu; Yu Zhang, Department of General Surgery, Wuxi Medical Center of Nanjing Medical University, Wuxi, People’s Republic of China, Tel +8613561164619 ; +8613793706777, Email ningzhouwxrmyy@163.com; yuzhangjs@yeah.netPurpose: 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.Keywords: gallstones, atherogenic index of plasma, national health and nutrition examination survey, cross-sectional studies, risk factor, machine learninghttps://www.dovepress.com/relative-fat-mass-and-physical-indices-as-predictors-of-gallstone-form-peer-reviewed-fulltext-article-IJGMgallstonesatherogenic index of plasmanational health and nutrition examination surveycrosssectional studiesrisk factormachine learning
spellingShingle Deng L
Wang S
Wan D
Zhang Q
Shen W
Liu X
Zhang Y
Relative Fat Mass and Physical Indices as Predictors of Gallstone Formation: Insights From Machine Learning and Logistic Regression
International Journal of General Medicine
gallstones
atherogenic index of plasma
national health and nutrition examination survey
crosssectional studies
risk factor
machine learning
title Relative Fat Mass and Physical Indices as Predictors of Gallstone Formation: Insights From Machine Learning and Logistic Regression
title_full Relative Fat Mass and Physical Indices as Predictors of Gallstone Formation: Insights From Machine Learning and Logistic Regression
title_fullStr Relative Fat Mass and Physical Indices as Predictors of Gallstone Formation: Insights From Machine Learning and Logistic Regression
title_full_unstemmed Relative Fat Mass and Physical Indices as Predictors of Gallstone Formation: Insights From Machine Learning and Logistic Regression
title_short Relative Fat Mass and Physical Indices as Predictors of Gallstone Formation: Insights From Machine Learning and Logistic Regression
title_sort relative fat mass and physical indices as predictors of gallstone formation insights from machine learning and logistic regression
topic gallstones
atherogenic index of plasma
national health and nutrition examination survey
crosssectional studies
risk factor
machine learning
url https://www.dovepress.com/relative-fat-mass-and-physical-indices-as-predictors-of-gallstone-form-peer-reviewed-fulltext-article-IJGM
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