ArticleWorld journal of hepatology2021
Machine learning models for predicting non-alcoholic fatty liver disease in the general United States population: NHANES database.
Article in World journal of hepatology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Applications of machine learning in the diagnosis of non-alcoholic fatty liver disease: a systematic review and meta-analysis.BMC gastroenterology · 2026Pooled it
- Diagnostic accuracy of artificial intelligence models for imaging detection of hepatic steatosis through systematic review and meta analysis.Scientific reports · 2025Pooled it
- Article
- 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
- Article
- The application of artificial intelligence in the intersection of metabolic dysfunction-associated steatotic liver disease and cardiovascular diseases.Frontiers in immunology · 2026Review
- Machine learning-based identification of targeted metabolomic biomarkers for early diagnosis and fibrosis-stage discrimination in metabolic dysfunction-associated steatotic liver disease.Frontiers in nutrition · 2026Article
- Development of a Neural Network to Detect Hepatic Steatosis in Metabolic Dysfunction-Associated Steatotic Liver Disease.Gastro hep advances · 2026Article
- Developing a non-invasive algorithm for the diagnosis of steatotic liver disease in primary healthcare: a retrospective cohort study.BMJ health & care informatics · 2025Article
- Article
- MOFI-FL, a novel score for detecting hepatic steatosis and predicting cardiometabolic mortality.Scientific reports · 2025Article
- Markers of body fat, the mediating role of alanine aminotransferase, and their association with the risk of metabolic dysfunction-associated steatotic liver disease.European journal of pediatrics · 2025Article
- Development of a novel deep learning method that transforms tabular input variables into images for the prediction of SLD.Scientific reports · 2025Article
- Precision Non-Alcoholic Fatty Liver Disease (NAFLD) Diagnosis: Leveraging Ensemble Machine Learning and Gender Insights for Cost-Effective Detection.Bioengineering (Basel, Switzerland) · 2024Article
- Evolutive Models, Algorithms and Predictive Parameters for the Progression of Hepatic Steatosis.Metabolites · 2024Article
- A dynamic machine learning model for prediction of NAFLD in a health checkup population: A longitudinal study.Heliyon · 2023Article
- Identifying Patients with Nonalcoholic Fatty Liver Disease in Primary Care: How and for What Benefit?Journal of clinical medicine · 2023Review
- Antioxidant and anti-inflammatory agents in chronic liver diseases: Molecular mechanisms and therapy.World journal of hepatology · 2023Review
- Machine-Learning Algorithm for Predicting Fatty Liver Disease in a Taiwanese Population.Journal of personalized medicine · 2022Article
- Machine learning models including insulin resistance indexes for predicting liver stiffness in United States population: Data from NHANES.Frontiers in public health · 2022Article
Corrections and comments
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundNon-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease, affecting over 30% of the United States population. Early patient identification using a simple method is highly desirable.
aimTo create machine learning models for predicting NAFLD in the general United States population.
methodsUsing the NHANES 1988-1994. Thirty NAFLD-related factors were included. The dataset was divided into the training (70%) and testing (30%) datasets. Twenty-four machine learning algorithms were applied to the training dataset. The best-performing models and another interpretable model (
resultsThere were 3235 participants (
conclusionNot every machine learning model is complex. Using a simpler model such as coarse trees, we can create an interpretable model for predicting NAFLD with only two predictors: fasting C-peptide and waist circumference. Although the simpler model does not have the best performance, its simplicity is useful in clinical practice.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.