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

Aug 5, 2026

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Objectives: Liver fibrosis from nonalcoholic fatty liver disease and metabolic disorders is common in patients with severe obesity, yet evidence on predicting fibrosis using noninvasive factors remains limited. This study evaluated the association between noninvasive variables and liver fibrosis using machine learning. Methods: Data from 512 bariatric surgery patients in Mashhad (Dec 2015–Sep 2021) were used to train four models (Naive Bayes, logistic regression, neural network, support vector machine) to distinguish fibrosis versus non-fibrosis groups. Results: Among 28 variables, six showed strong diagnostic AUC using 2D shear wave elastography. Conclusion: The six noninvasive factors demonstrated superior predictive performance with clinically useful accuracy for fibrosis diagnosis and prognosis.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages21

Reading Time53 min

AccessFree to download

Introduction

Background and clinical importance

Limitations of liver biopsy and need for noninvasive tools

Methods

Study population and setting

Model development

Results

Selected variables and diagnostic performance

Sensitivity of models

Conclusion

This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: [http://dx.doi.org/10.1007/](http://dx.doi.org/10.1007/)[ ](http://dx.doi.org/10.1007/)s40200-025-01564-1 Predictive performance of noninvasive factors for liver fibrosis in severe obesity: a screening based on machine learning models Tannaz Jamialahmadi1,2, Mehdi Azizmohammad Looha3, Sara Jangjoo4, Nima Emami4, Mohammed Altigani Abdalla5, Mohammadreza Ganjali2, Sepideh Salehabadi4, Sercan Karav6, Thozhukat Sathyapalan7, Ali H. Eid8, Ali Jangjoo9, Amirhossein Sahebkar10,11,12* 1 Pharmaceutical Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran 2 Medical Toxicology Research Center, Mashhad University of Medical Sciences, Mashhad, Iran 3 Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran 4 School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran 5 Allam Diabetes Centre, Academic Diabetes, Endocrinology and Metabolism, Hull York Medical School (HYMS), University of Hull, Hull, UK 6 Department of Molecular Biology and Genetics, Canakkale Onsekiz Mart University, Canakkale 17100, Turkey 7 Academic Diabetes, Endocrinology and Metabolism, Hull York Medical School, University of Hull, Hull, United Kingdom 8 Department of Basic Medical Sciences, College of Medicine, QU Health, Qatar University, Doha, Qatar 9 Surgical Oncology Research Center, Imam Reza Hospital, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran 10 Center for Global Health Research, Saveetha Medical College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, India 11 Biotechnology Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran 12 Applied Biomedical Research Center, Mashhad University of Medical Sciences, Mashhad, Iran Correspondence: Amirhossein Sahebkar; [amir_saheb2000@yahoo.com](amir_saheb2000@yahoo.com); [Sahebkara@mums.ac.ir](Sahebkara@mums.ac.ir) [Competing interests](Competing interests: None)[: None](Competing interests: None) Abstract Objectives Liver fibrosis resulting from nonalcoholic fatty liver disease (NAFLD) and metabolic disorders is highly prevalent in patients with severe obesity and poses a significant global health challenge. However, thereis 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. Methods This 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 the 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), Results Among the 28 variables considered, six variables including (fasting blood sugar (FBS), skeletal muscle mass (SMM), hemoglobin, alanine transaminase (ALT), aspartate transaminase (AST) and triglyceride) showed high area under the curve (AUC) values for 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, highest sensitivity were reported with SVM (0 .83, 95% CI: 0.72, 0.91) and NB (0 .66, 95% CI: 0.53, 0.77) models, respectively. Conclusion The predictive performan

FAQs

What was the main objective of this study?

To assess how noninvasive factors relate to liver fibrosis and to predict fibrosis using machine learning models.

How many patients were included and how were they selected?

The study included 512 patients who underwent bariatric surgery at an outpatient clinic in Mashhad between December 2015 and September 2021.

Which noninvasive variables showed strong diagnostic performance?

Fasting blood sugar, skeletal muscle mass, hemoglobin, alanine transaminase, aspartate transaminase, and triglyceride were among the variables with high AUC for diagnosing liver fibrosis using 2D shear wave elastography.

Aug 5, 2026

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