ArticleComputational and mathematical methods in medicine2014
Use of CHAID decision trees to formulate pathways for the early detection of metabolic syndrome in young adults.
Article in Computational and mathematical methods in medicine, 2014. 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.
- Clinical correlates of lipoprotein (a) and apolipoprotein B levels in patients with dyslipidemia and cardiovascular disease.Frontiers in cardiovascular medicine · 2026Article
- Predicting unplanned readmissions in neurological patients: A large-scale AI-driven analysis using CHAID decision trees.PloS one · 2026Article
- Employing broad learning and non-invasive risk factor to improve the early diagnosis of metabolic syndrome.iScience · 2024Article
- Early Identification of Metabolic Syndrome in Adults of Jiaxing, China: Utilizing a Multifactor Logistic Regression Model.Diabetes, metabolic syndrome and obesity : targets and therapy · 2024Article
- 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
- Risk factors, time to onset and recurrence of delirium in a mixed medical-surgical ICU population: A secondary analysis using Cox and CHAID decision tree modeling.EXCLI journal · 2022Article
- Prediction of neonatal deaths in NICUs: development and validation of machine learning models.BMC medical informatics and decision making · 2021Article
- Refinement of prognosis and the effect of azacitidine in intermediate-risk myelodysplastic syndromes.Blood cancer journal · 2021Article
- New Simplified Diagnostic Decision Trees for the Detention of Metabolic Syndrome in the Elderly.International journal of environmental research and public health · 2020Article
- Research on diagnosis-related group grouping of inpatient medical expenditure in colorectal cancer patients based on a decision tree model.World journal of clinical cases · 2020Article
- Educational intervention improves fruit and vegetable intake in young adults with metabolic syndrome components.Nutrition research (New York, N.Y.) · 2019Article
- Validation of a non-invasive method for the early detection of metabolic syndrome: a diagnostic accuracy test in a working population.BMJ open · 2018Article
- Data mining for the identification of metabolic syndrome status.EXCLI journal · 2018Review
- Identification of Risk Factors of Multidrug-Resistant Tuberculosis by using Classification Tree Method.The American journal of tropical medicine and hygiene · 2017Article
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Abstract
Metabolic syndrome (MetS) in young adults (age 20-39) is often undiagnosed. A simple screening tool using a surrogate measure might be invaluable in the early detection of MetS. Methods. A chi-squared automatic interaction detection (CHAID) decision tree analysis with waist circumference user-specified as the first level was used to detect MetS in young adults using data from the National Health and Nutrition Examination Survey (NHANES) 2009-2010 Cohort as a representative sample of the United States population (n = 745). Results. Twenty percent of the sample met the National Cholesterol Education Program Adult Treatment Panel III (NCEP) classification criteria for MetS. The user-specified CHAID model was compared to both CHAID model with no user-specified first level and logistic regression based model. This analysis identified waist circumference as a strong predictor in the MetS diagnosis. The accuracy of the final model with waist circumference user-specified as the first level was 92.3% with its ability to detect MetS at 71.8% which outperformed comparison models. Conclusions. Preliminary findings suggest that young adults at risk for MetS could be identified for further followup based on their waist circumference. Decision tree methods show promise for the development of a preliminary detection algorithm for MetS.
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