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Use of CHAID Decision Trees to Formulate Pathways for the Early Detection of Metabolic Syndrome in Young Adults

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-specif...

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Autores principales: Miller, Brian, Fridline, Mark, Liu, Pei-Yang, Marino, Deborah
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4003739/
https://www.ncbi.nlm.nih.gov/pubmed/24817904
http://dx.doi.org/10.1155/2014/242717
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author Miller, Brian
Fridline, Mark
Liu, Pei-Yang
Marino, Deborah
author_facet Miller, Brian
Fridline, Mark
Liu, Pei-Yang
Marino, Deborah
author_sort Miller, Brian
collection PubMed
description 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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spelling pubmed-40037392014-05-11 Use of CHAID Decision Trees to Formulate Pathways for the Early Detection of Metabolic Syndrome in Young Adults Miller, Brian Fridline, Mark Liu, Pei-Yang Marino, Deborah Comput Math Methods Med Research Article 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. Hindawi Publishing Corporation 2014 2014-04-10 /pmc/articles/PMC4003739/ /pubmed/24817904 http://dx.doi.org/10.1155/2014/242717 Text en Copyright © 2014 Brian Miller et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
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
title Use of CHAID Decision Trees to Formulate Pathways for the Early Detection of Metabolic Syndrome in Young Adults
title_full Use of CHAID Decision Trees to Formulate Pathways for the Early Detection of Metabolic Syndrome in Young Adults
title_fullStr Use of CHAID Decision Trees to Formulate Pathways for the Early Detection of Metabolic Syndrome in Young Adults
title_full_unstemmed Use of CHAID Decision Trees to Formulate Pathways for the Early Detection of Metabolic Syndrome in Young Adults
title_short Use of CHAID Decision Trees to Formulate Pathways for the Early Detection of Metabolic Syndrome in Young Adults
title_sort use of chaid decision trees to formulate pathways for the early detection of metabolic syndrome in young adults
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4003739/
https://www.ncbi.nlm.nih.gov/pubmed/24817904
http://dx.doi.org/10.1155/2014/242717
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