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Use of CHAID Decision Trees to Formulate Pathways for the Early Detection of Metabolic Syndrome in Young Adults
Joint Authors
Marino, Deborah
Fridline, Mark
Miller, Brian
Liu, Pei-Yang
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-04-10
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Miller, Brian& Fridline, Mark& Liu, Pei-Yang& Marino, Deborah. 2014. Use of CHAID Decision Trees to Formulate Pathways for the Early Detection of Metabolic Syndrome in Young Adults. Computational and Mathematical Methods in Medicine،Vol. 2014, no. 2014, pp.1-7.
https://search.emarefa.net/detail/BIM-456732
Modern Language Association (MLA)
Miller, Brian…[et al.]. Use of CHAID Decision Trees to Formulate Pathways for the Early Detection of Metabolic Syndrome in Young Adults. Computational and Mathematical Methods in Medicine No. 2014 (2014), pp.1-7.
https://search.emarefa.net/detail/BIM-456732
American Medical Association (AMA)
Miller, Brian& Fridline, Mark& Liu, Pei-Yang& Marino, Deborah. Use of CHAID Decision Trees to Formulate Pathways for the Early Detection of Metabolic Syndrome in Young Adults. Computational and Mathematical Methods in Medicine. 2014. Vol. 2014, no. 2014, pp.1-7.
https://search.emarefa.net/detail/BIM-456732
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-456732