ArticleJMIR medical informatics2020
Predicting Metabolic Syndrome With Machine Learning Models Using a Decision Tree Algorithm: Retrospective Cohort Study.
Article in JMIR medical informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Beyond Feature Selection: Interpretable Machine Learning for Mechanistic Insights in Metabolomics.Biology · 2026Review
- Urine Metabolomics and Machine Learning Identify Metabolic Features and Potential Biomarkers of HTLV-1-Associated Myelopathy (HAM).International journal of molecular sciences · 2026Article
- Predicting Metabolic Dysfunction-Associated Fatty Liver Disease Phenotypes Among Adults: 2-Stage Contrastive Learning Method.JMIR medical informatics · 2025Article
- Integrated Patient Digital and Biomimetic Twins for Precision Medicine: A Perspective.Seminars in liver disease · 2025Review
- Prediction of metabolic syndrome using machine learning approaches based on genetic and nutritional factors: a 14-year prospective-based cohort study.BMC medical genomics · 2024Article
- Health Star Rating of Nonalcoholic, Packaged, and Ready-to-Drink Beverages in Türkiye: A Decision Tree Model Study.Preventive nutrition and food science · 2024Article
- Automated Machine Learning to Develop Predictive Models of Metabolic Syndrome in Patients with Periodontal Disease.Diagnostics (Basel, Switzerland) · 2023Article
- Comparison of diagnostic accuracy of models combining the renal biomarkers in predicting renal scarring in pediatric population with vesicoureteral reflux (VUR).Irish journal of medical science · 2023Article
- Exploring the Potential Performance of Fibroscan for Predicting and Evaluating Metabolic Syndrome using a Feature Selected Strategy of Machine Learning.Metabolites · 2023Article
- Development of a Metabolic Syndrome Classification and Prediction Model for Koreans Using Deep Learning Technology: The Korea National Health and Nutrition Examination Survey (KNHANES) (2013-2018).Clinical nutrition research · 2023Article
- Application of C5.0 Algorithm for the Assessment of Perceived Stress in Healthcare Professionals Attending COVID-19.Brain sciences · 2023Article
- IoT Framework for a Decision-Making System of Obesity and Overweight Extrapolation among Children, Youths, and Adults.Life (Basel, Switzerland) · 2022Article
- Machine learning-aided risk prediction for metabolic syndrome based on 3 years study.Scientific reports · 2022Article
- "Big Data" Approaches for Prevention of the Metabolic Syndrome.Frontiers in genetics · 2022Review
- Using Machine Learning Techniques to Predict Factors Contributing to the Incidence of Metabolic Syndrome in Tehran: Cohort Study.JMIR public health and surveillance · 2021Article
- Article
- Prediction of neonatal deaths in NICUs: development and validation of machine learning models.BMC medical informatics and decision making · 2021Article
- Identify the Characteristics of Metabolic Syndrome and Non-obese Phenotype: Data Visualization and a Machine Learning Approach.Frontiers in medicine · 2021Article
- Machine-Learning Monitoring System for Predicting Mortality Among Patients With Noncancer End-Stage Liver Disease: Retrospective Study.JMIR medical informatics · 2020Article
- Development of an Online Health Care Assessment for Preventive Medicine: A Machine Learning Approach.Journal of medical Internet research · 2020Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMetabolic syndrome is a cluster of disorders that significantly influence the development and deterioration of numerous diseases. FibroScan is an ultrasound device that was recently shown to predict metabolic syndrome with moderate accuracy. However, previous research regarding prediction of metabolic syndrome in subjects examined with FibroScan has been mainly based on conventional statistical models. Alternatively, machine learning, whereby a computer algorithm learns from prior experience, has better predictive performance over conventional statistical modeling.
objectiveWe aimed to evaluate the accuracy of different decision tree machine learning algorithms to predict the state of metabolic syndrome in self-paid health examination subjects who were examined with FibroScan.
methodsMultivariate logistic regression was conducted for every known risk factor of metabolic syndrome. Principal components analysis was used to visualize the distribution of metabolic syndrome patients. We further applied various statistical machine learning techniques to visualize and investigate the pattern and relationship between metabolic syndrome and several risk variables.
resultsObesity, serum glutamic-oxalocetic transaminase, serum glutamic pyruvic transaminase, controlled attenuation parameter score, and glycated hemoglobin emerged as significant risk factors in multivariate logistic regression. The area under the receiver operating characteristic curve values for classification and regression trees and for the random forest were 0.831 and 0.904, respectively.
conclusionsMachine learning technology facilitates the identification of metabolic syndrome in self-paid health examination subjects with high accuracy.
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