ArticleTheScientificWorldJournal2015
Predicting Metabolic Syndrome Using the Random Forest Method.
Article in TheScientificWorldJournal, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed, 61 citations in OpenAlex.
- Vision transformer based interpretable metabolic syndrome classification using retinal Images.NPJ digital medicine · 2025Article
- Non-Targeted Metabolomics Analysis of Small Molecular Metabolites in Refrigerated Goose Breast Meat.Veterinary sciences · 2024Article
- 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
- Prediction of properties of boron [Formula: see text]-icosahedral nanosheet by bond-addictive [Formula: see text]-polynomial.Scientific reports · 2024Article
- Article
- Predictive analysis of metabolic syndrome based on 5-years continuous physical examination data.Scientific reports · 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
- Identifying Metabolic Syndrome Easily and Cost Effectively Using Non-Invasive Methods with Machine Learning Models.Diabetes, metabolic syndrome and obesity : targets and therapy · 2023Article
- Machine Learning Approach for Metabolic Syndrome Diagnosis Using Explainable Data-Augmentation-Based Classification.Diagnostics (Basel, Switzerland) · 2022Article
- Evaluating machine learning-powered classification algorithms which utilize variants in the GCKR gene to predict metabolic syndrome: Tehran Cardio-metabolic Genetics Study.Journal of translational medicine · 2022Article
- Prediction of metabolic and pre-metabolic syndromes using machine learning models with anthropometric, lifestyle, and biochemical factors from a middle-aged population in Korea.BMC public health · 2022Article
- Machine learning-aided risk prediction for metabolic syndrome based on 3 years study.Scientific reports · 2022Article
- A novel multistage ensemble approach for prediction and classification of diabetes.Frontiers in physiology · 2022Article
- Prediction and predictor elucidation of metabolic syndrome onset among young workers using machine learning techniques: A nationwide study in Japan.Environmental and occupational health practice · 2022Article
- iPMI: Machine Learning-Aided Identification of Parametrial Invasion in Women with Early-Stage Cervical Cancer.Diagnostics (Basel, Switzerland) · 2021Article
- Positive influence of gut microbiota on the effects of Korean red ginseng in metabolic syndrome: a randomized, double-blind, placebo-controlled clinical trial.The EPMA journal · 2021Article
- Novel Gene Signatures Predicting Primary Non-response to Infliximab in Ulcerative Colitis: Development and Validation Combining Random Forest With Artificial Neural Network.Frontiers in medicine · 2021Article
- Development and internal validation of risk prediction model of metabolic syndrome in oil workers.BMC public health · 2020Article
- Article
- UHPLC-MS/MS-Based Nontargeted Metabolomics Analysis Reveals Biomarkers Related to the Freshness of Chilled Chicken.Foods (Basel, Switzerland) · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimsThis study proposes a computational method for determining the prevalence of metabolic syndrome (MS) and to predict its occurrence using the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) criteria. The Random Forest (RF) method is also applied to identify significant health parameters. MATERIALS AND
methodsWe used data from 5,646 adults aged between 18-78 years residing in Bangkok who had received an annual health check-up in 2008. MS was identified using the NCEP ATP III criteria. The RF method was applied to predict the occurrence of MS and to identify important health parameters surrounding this disorder.
resultsThe overall prevalence of MS was 23.70% (34.32% for males and 17.74% for females). RF accuracy for predicting MS in an adult Thai population was 98.11%. Further, based on RF, triglyceride levels were the most important health parameter associated with MS.
conclusionRF was shown to predict MS in an adult Thai population with an accuracy >98% and triglyceride levels were identified as the most informative variable associated with MS. Therefore, using RF to predict MS may be potentially beneficial in identifying MS status for preventing the development of diabetes mellitus and cardiovascular diseases.
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Registered trials
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.