title: Use of CHAID decision trees to formulate pathways for the early detection of metabolic syndrome in young adults.
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.
authors.affiliation: School of Nutrition and Dietetics, College of Health Professions, The University of Akron, Akron, OH 44325-6102, USA.
authors.forename: Brian
authors.identifier: 0000-0003-4160-4719
authors.initials: B
authors.lastname: Miller
authors.affiliation: Department of Statistics, College of Arts and Sciences, University of Akron, Akron, OH 44325-1913, USA.
authors.forename: Mark
authors.identifier: 0000-0001-6792-4636
authors.initials: M
authors.lastname: Fridline
authors.affiliation: School of Nutrition and Dietetics, College of Health Professions, The University of Akron, Akron, OH 44325-6102, USA.
authors.forename: Pei-Yang
authors.initials: PY
authors.lastname: Liu
authors.affiliation: School of Nutrition and Dietetics, College of Health Professions, The University of Akron, Akron, OH 44325-6102, USA.
authors.forename: Deborah
authors.identifier: 0000-0002-2663-7611
authors.initials: D
authors.lastname: Marino
mesh_terms: D000328:Adult; D015331:Cohort Studies; D020000:Decision Support Systems, Clinical; D003663:Decision Trees; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008403:Mass Screening / Q000379:methods; D008490:Medical Informatics; D024821:Metabolic Syndrome / Q000175:diagnosis*; D012307:Risk Factors; D012984:Software; D014481:United States; D055105:Waist Circumference; D055815:Young Adult