ArticleIranian Red Crescent medical journal2016
Prediction and Diagnosis of Non-Alcoholic Fatty Liver Disease (NAFLD) and Identification of Its Associated Factors Using the Classification Tree Method.
Article in Iranian Red Crescent medical journal, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 27 citations in OpenAlex.
- Liver fat scores do not reflect interventional changes in liver fat content induced by high-protein diets.Scientific reports · 2021Trial
- Liver Fat Scores Moderately Reflect Interventional Changes in Liver Fat Content by a Low-Fat Diet but Not by a Low-Carb Diet.Nutrients · 2018Trial
- Development of a machine learning model to identify individuals with VCTE-derived at-risk MASH in a Spanish Mediterranean region.Frontiers in digital health · 2026Article
- Machine-Learning Application for Predicting Metabolic Dysfunction-Associated Steatotic Liver Disease Using Laboratory and Body Composition Indicators.Archives of Iranian medicine · 2024Article
- Liver Fat Scores for Noninvasive Diagnosis and Monitoring of Nonalcoholic Fatty Liver Disease in Epidemiological and Clinical Studies.Journal of clinical and translational hepatology · 2023Review
- The Prevalence of Non-Alcoholic Fatty Liver Disease in Iranian Children and Adult Population: A Systematic Review and Meta-Analysis.Iranian journal of public health · 2023Review
- Application of machine learning in predicting non-alcoholic fatty liver disease using anthropometric and body composition indices.Scientific reports · 2023Article
- Sodium in relation with nonalcoholic fatty liver disease: A systematic review and meta-analysis of observational studies.Food science & nutrition · 2022Review
- Machine learning models for predicting non-alcoholic fatty liver disease in the general United States population: NHANES database.World journal of hepatology · 2021Article
- Current-Visit and Next-Visit Prediction for Fatty Liver Disease With a Large-Scale Dataset: Model Development and Performance Comparison.JMIR medical informatics · 2021Article
- Assessment of Classification Models and Relevant Features on Nonalcoholic Steatohepatitis Using Random Forest.Entropy (Basel, Switzerland) · 2021Article
- Implementation of Combinational Deep Learning Algorithm for Non-alcoholic Fatty Liver Classification in Ultrasound Images.Journal of biomedical physics & engineering · 2021Article
- Non-alcoholic Fatty Liver and Liver Fibrosis Predictive Analytics: Risk Prediction and Machine Learning Techniques for Improved Preventive Medicine.Journal of medical systems · 2021Article
- Liver Resection for Hepatocellular Carcinoma in Non-alcoholic Fatty Liver Disease: a Multicenter Propensity Matching Analysis with HBV-HCC.Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract · 2020Article
- [Predictive value of body mass index combined with waist circumference for new-onset nonalcoholic fatty liver disease in patients with type 2 diabetes mellitus].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2019Article
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Authors and funding
4 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundNon-alcoholic fatty liver disease (NAFLD) is the most common form of liver disease in many parts of the world.
objectivesThe aim of the present study was to identify the most important factors influencing NAFLD using a classification tree (CT) to predict the probability of NAFLD. PATIENTS AND
methodsThis cross-sectional study was conducted in Kavar, a town in the south of Fars province, Iran. A total of 1,600 individuals were selected for the study via the stratified method and multiple-stage cluster random sampling. A total of 30 demographic and clinical variables were measured for each individual. Participants were divided into two datasets: testing and training. We used the training dataset (1,120 individuals) to build the CT and the testing dataset (480 individuals) to assess the CT. The CT was also used to estimate class and to predict fatty liver occurrence.
resultsNAFLD was diagnosed in 22% of the individuals in the sample. Our findings revealed that the following variables, based on univariate analysis, had a significant association with NAFLD: marital status, history of hepatitis B vaccine, history of surgery, body mass index (BMI), waist-hip ratio (WHR), systolic blood pressure (SBP), diastolic blood pressure (DBP), high-density lipoprotein (HDL), triglycerides (TG), alanine aminotransferase (ALT), cholesterol (CHO0, aspartate aminotransferase (AST), glucose (GLU), albumin (AL), and age (P < 0.05). The main affecting variables for predicting NAFLD based on the CT and in order of importance were as follows: BMI, WHR, triglycerides, glucose, SBP, and alanine aminotransferase. The goodness of fit model based on the training and testing datasets were as follows: prediction accuracy (80%, 75%), sensitivity (74%, 73%), specificity (83%, 77%), and the area under the receiver operating characteristic (ROC) curve (78%, 75%), respectively.
conclusionsThe CT is a suitable and easy-to-interpret approach for decision-making and predicting NAFLD.
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