Predictive performance of noninvasive factors for liver fibrosis in severe obesity: a screening based on machine learning models

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dc.authoridAzizmohammad Looha, Mehdi/0000-0002-0700-1431
dc.contributor.authorJamialahmadi, Tannaz
dc.contributor.authorLooha, Mehdi Azizmohammad
dc.contributor.authorJangjoo, Sara
dc.contributor.authorEmami, Nima
dc.contributor.authorAbdalla, Mohammed Altigani
dc.contributor.authorGanjali, Mohammadreza
dc.contributor.authorSalehabadi, Sepideh
dc.date.accessioned2025-05-29T02:58:02Z
dc.date.available2025-05-29T02:58:02Z
dc.date.issued2025
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractObjectivesLiver fibrosis resulting from nonalcoholic fatty liver disease (NAFLD) and metabolic disorders is highly prevalent in patients with severe obesity and poses a significant health challenge. However, there is a lack of data on the effectiveness of noninvasive factors in predicting liver fibrosis. Therefore, this study aimed to assess the relationship between these factors and liver fibrosis through a machine learning approach.MethodsThis study involved 512 patients who underwent bariatric surgery at an outpatient clinic in Mashhad, Iran, between December 2015 and September 2021. Patients were divided into fibrosis and non-fibrosis groups and demographic, clinical, and laboratory variables were applied to develop four machine learning models: Naive Bayes (NB), logistic regression (LR), Neural Network (NN) and Support Vector Machine (SVM),ResultsAmong the 28 variables considered, six variables including (fasting blood sugar (FBS), skeletal muscle mass (SMM), hemoglobin, alanine transaminase (ALT), aspartate transaminase (AST) and triglycerides) showed high area under the curve (AUC) values for the diagnosis of liver fibrosis using 2D shear wave elastography (SWE) with LR (0.73, 95% CI: 0.65, 0.81) and SVM (0.72, 59% CI: 0.64, 0.80) models. Furthermore, the highest sensitivities were reported with SVM (0.83, 95% CI: 0.72, 0.91) and NB (0.66, 95% CI: 0.53, 0.77) models, respectively.ConclusionThe predictive performance of six noninvasive factors of liver fibrosis was significantly superior to other factors, showing high application and accuracy in the diagnosis and prognosis of liver fibrosis.
dc.identifier.doi10.1007/s40200-025-01564-1
dc.identifier.issn2251-6581
dc.identifier.issue1
dc.identifier.pmid39834350
dc.identifier.scopus2-s2.0-85218263155
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1007/s40200-025-01564-1
dc.identifier.urihttps://hdl.handle.net/20.500.12428/30251
dc.identifier.volume24
dc.identifier.wosWOS:001399476200001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherSpringer Int Publ Ag
dc.relation.ispartofJournal of Diabetes and Metabolic Disorders
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250529
dc.subjectLiver fibrosis
dc.subjectNAFLD
dc.subjectNASH
dc.subjectMachin learning
dc.subjectLS
dc.titlePredictive performance of noninvasive factors for liver fibrosis in severe obesity: a screening based on machine learning models
dc.typeArticle

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