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

Tannaz Jamialahmadi; Mehdi Azizmohammad Looha; Sara Jangjoo; Nima Emami; Mohammed Altigani Abdalla; Mohammadreza Ganjali; Sepideh Salehabadi; Sercan Karav; Thozhukat Sathyapalan; Ali H Eid; Ali Jangjoo; Amirhossein Sahebkar

doi:10.1007/s40200-025-01564-1

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

J Diabetes Metab Disord. 2025 Jan 18;24(1):54. doi: 10.1007/s40200-025-01564-1. eCollection 2025 Jun.

Authors

Affiliations

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, UK.
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 Institute of Medical and Technical Sciences, Saveetha Medical College and Hospitals, 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.

PMID: 39834350
PMCID: PMC11741961 (available on 2026-01-18)
DOI: 10.1007/s40200-025-01564-1

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 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.

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 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 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.

Conclusion: The 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.

Supplementary information: The online version contains supplementary material available at 10.1007/s40200-025-01564-1.

Keywords: LS; Liver fibrosis; Machin learning; NAFLD; NASH.

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