ArticleScientific reports2023
Application of machine learning in predicting non-alcoholic fatty liver disease using anthropometric and body composition indices.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 40 citations in OpenAlex.
- Diagnostic accuracy of artificial intelligence models for imaging detection of hepatic steatosis through systematic review and meta analysis.Scientific reports · 2025Pooled it
- Applying Artificial Intelligence to Childhood Obesity: T2DM and MASLD Risk Predictive Models.Diagnostics (Basel, Switzerland) · 2026Review
- A Transformer-Residual Network-Integrated Deep Learning Model for Digital Pathology-Based Grading and Risk Stratification of Metabolic Dysfunction-Associated Steatotic Liver Disease-Related Liver Fibrosis.Clinical and translational gastroenterology · 2026Article
- Attention-based graph neural network framework for non-invasive CAP score prediction in fatty liver disease via body modeling.Physical and engineering sciences in medicine · 2026Article
- Diagnosis and grading of steatotic liver disease via clinical and laboratory data using machine learning.Scientific reports · 2026Article
- The application of artificial intelligence in the intersection of metabolic dysfunction-associated steatotic liver disease and cardiovascular diseases.Frontiers in immunology · 2026Review
- Development and validation of a high-performance clinical predictive model for early identification of non-alcoholic fatty liver disease.Frontiers in physiology · 2026Article
- Machine Learning Models to Predict Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) With Simple Anthropometric and Biochemical Variables: A Cross-Sectional Study in US Population.International journal of hepatology · 2026Article
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- Machine Learning-Based Prediction of Decompensation in Hepatitis B Virus-Related Cirrhosis.Diagnostics (Basel, Switzerland) · 2025Article
- Big Data Analytics in Large Cohorts: Opportunities and Challenges for Research in Hepatology.Seminars in liver disease · 2025Review
- Development of a novel deep learning method that transforms tabular input variables into images for the prediction of SLD.Scientific reports · 2025Article
- Frequency and risk factors of metabolic associated fatty liver disease among medical students in Egypt.Scientific reports · 2025Article
- Machine learning models for predicting metabolic dysfunction-associated steatotic liver disease prevalence using basic demographic and clinical characteristics.Journal of translational medicine · 2025Article
- Potential alternative and novel biomarkers for paediatric MAFLD: exploratory evidence from a Chinese cohort.BMC gastroenterology · 2025Article
- Investigation of predictive factors for fatty liver in children and adolescents using artificial intelligence.Frontiers in pediatrics · 2025Article
- Editorial: Body composition assessment and future disease risk.Frontiers in medicine · 2025Article
- From traditional metabolic markers to ensemble learning: comparative application of machine learning models for predicting NAFLD risk in adolescents.Frontiers in endocrinology · 2025Article
- Machine-Learning Application for Predicting Metabolic Dysfunction-Associated Steatotic Liver Disease Using Laboratory and Body Composition Indicators.Archives of Iranian medicine · 2024Article
Corrections and comments
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Authors and funding
10 authors at 7 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease, which can progress from simple steatosis to advanced cirrhosis and hepatocellular carcinoma. Clinical diagnosis of NAFLD is crucial in the early stages of the disease. The main aim of this study was to apply machine learning (ML) methods to identify significant classifiers of NAFLD using body composition and anthropometric variables. A cross-sectional study was carried out among 513 individuals aged 13 years old or above in Iran. Anthropometric and body composition measurements were performed manually using body composition analyzer InBody 270. Hepatic steatosis and fibrosis were determined using a Fibroscan. ML methods including k-Nearest Neighbor (kNN), Support Vector Machine (SVM), Radial Basis Function (RBF) SVM, Gaussian Process (GP), Random Forest (RF), Neural Network (NN), Adaboost and Naïve Bayes were examined for model performance and to identify anthropometric and body composition predictors of fatty liver disease. RF generated the most accurate model for fatty liver (presence of any stage), steatosis stages and fibrosis stages with 82%, 52% and 57% accuracy, respectively. Abdomen circumference, waist circumference, chest circumference, trunk fat and body mass index were among the most important variables contributing to fatty liver disease. ML-based prediction of NAFLD using anthropometric and body composition data can assist clinicians in decision making. ML-based systems provide opportunities for NAFLD screening and early diagnosis, especially in population-level and remote areas.
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