ArticleFrontiers in public health2022
A Machine Learning Based Framework to Identify and Classify Non-alcoholic Fatty Liver Disease in a Large-Scale Population.
Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it, 33 citations in OpenAlex.
- 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
- Interpretable machine learning models based on CMR radiomics for predicting left ventricular diastolic dysfunction in patients with metabolic-associated steatotic liver disease and type 2 diabetes mellitus.Journal of diabetes investigation · 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
- 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
- Predictive modeling of axillary web syndrome in Chinese postoperative breast cancer patients using interpretable machine learning.Frontiers in oncology · 2026Article
- 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
- [Mediating role of insulin resistance in the relationship between hypertension and NAFLD and construction of its risk prediction model].Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2025Article
- Application of Machine Learning Models in Predicting Non-Alcoholic Fatty Liver Disease Among Inactive Chronic Hepatitis B Patients: A Cross-Sectional Analysis.Journal of clinical medicine · 2025Article
- FF Swin-Unet: a strategy for automated segmentation and severity scoring of NAFLD.BMC medical imaging · 2025Article
- Prediction of Insulin Resistance in Nondiabetic Population Using LightGBM and Cohort Validation of Its Clinical Value: Cross-Sectional and Retrospective Cohort Study.JMIR medical informatics · 2025Article
- Machine learning models for predicting metabolic dysfunction-associated steatotic liver disease prevalence using basic demographic and clinical characteristics.Journal of translational medicine · 2025Article
- Mitochondrial mt12361A>G increased risk of metabolic dysfunction-associated steatotic liver disease among non-diabetes.World journal of gastroenterology · 2025Article
- Diabetic peripheral neuropathy detection of type 2 diabetes using machine learning from TCM features: a cross-sectional study.BMC medical informatics and decision making · 2025Observational
- Investigation of predictive factors for fatty liver in children and adolescents using artificial intelligence.Frontiers in pediatrics · 2025Article
- Precision Non-Alcoholic Fatty Liver Disease (NAFLD) Diagnosis: Leveraging Ensemble Machine Learning and Gender Insights for Cost-Effective Detection.Bioengineering (Basel, Switzerland) · 2024Article
- Artificial Intelligence in Identifying Patients With Undiagnosed Nonalcoholic Steatohepatitis.Journal of health economics and outcomes research · 2024Article
- A dynamic machine learning model for prediction of NAFLD in a health checkup population: A longitudinal study.Heliyon · 2023Article
- Machine-Learning Algorithm for Predicting Fatty Liver Disease in a Taiwanese Population.Journal of personalized medicine · 2022Article
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Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Non-alcoholic fatty liver disease (NAFLD) is a common serious health problem worldwide, which lacks efficient medical treatment. We aimed to develop and validate the machine learning (ML) models which could be used to the accurate screening of large number of people. This paper included 304,145 adults who have joined in the national physical examination and used their questionnaire and physical measurement parameters as model's candidate covariates. Absolute shrinkage and selection operator (LASSO) was used to feature selection from candidate covariates, then four ML algorithms were used to build the screening model for NAFLD, used a classifier with the best performance to output the importance score of the covariate in NAFLD. Among the four ML algorithms, XGBoost owned the best performance (accuracy = 0.880, precision = 0.801, recall = 0.894, F-1 = 0.882, and AUC = 0.951), and the importance ranking of covariates is accordingly BMI, age, waist circumference, gender, type 2 diabetes, gallbladder disease, smoking, hypertension, dietary status, physical activity, oil-loving and salt-loving. ML classifiers could help medical agencies achieve the early identification and classification of NAFLD, which is particularly useful for areas with poor economy, and the covariates' importance degree will be helpful to the prevention and treatment of NAFLD.
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