Binary classification of dyslipidemia from the waist-to-hip ratio and body mass index: a comparison of linear, logistic, and CART models.

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serval:BIB_31810
Type
Article: article from journal or magazin.
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Publications
Institution
Title
Binary classification of dyslipidemia from the waist-to-hip ratio and body mass index: a comparison of linear, logistic, and CART models.
Journal
BMC medical research methodology
Author(s)
Costanza M.C., Paccaud F.
ISSN
1471-2288[electronic]
ISSN-L
1471-2288
Publication state
Published
Issued date
2004
Peer-reviewed
Oui
Volume
4
Pages
7 [10 p.]
Language
english
Abstract
BACKGROUND: We sought to improve upon previously published statistical modeling strategies for binary classification of dyslipidemia for general population screening purposes based on the waist-to-hip circumference ratio and body mass index anthropometric measurements. METHODS: Study subjects were participants in WHO-MONICA population-based surveys conducted in two Swiss regions. Outcome variables were based on the total serum cholesterol to high density lipoprotein cholesterol ratio. The other potential predictor variables were gender, age, current cigarette smoking, and hypertension. The models investigated were: (i) linear regression; (ii) logistic classification; (iii) regression trees; (iv) classification trees (iii and iv are collectively known as "CART"). Binary classification performance of the region-specific models was externally validated by classifying the subjects from the other region. RESULTS: Waist-to-hip circumference ratio and body mass index remained modest predictors of dyslipidemia. Correct classification rates for all models were 60-80%, with marked gender differences. Gender-specific models provided only small gains in classification. The external validations provided assurance about the stability of the models. CONCLUSIONS: There were no striking differences between either the algebraic (i, ii) vs. non-algebraic (iii, iv), or the regression (i, iii) vs. classification (ii, iv) modeling approaches. Anticipated advantages of the CART vs. simple additive linear and logistic models were less than expected in this particular application with a relatively small set of predictor variables. CART models may be more useful when considering main effects and interactions between larger sets of predictor variables.
Keywords
Adult, Body Constitution, Body Mass Index, Diagnostic Techniques, Cardiovascular, Female, Humans, Hyperlipidemias, Linear Models, Logistic Models, Male, Middle Aged, Models, Statistical, Predictive Value of Tests, Prevalence, Sensitivity and Specificity, Switzerland
Pubmed
Open Access
Yes
Create date
19/11/2007 12:30
Last modification date
20/08/2019 13:16
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