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

• Main Authors: Paccaud Fred, Costanza Michael C
• Format: Article
• Language: English
• Published: BMC 2004-04-01
• Series: BMC Medical Research Methodology
• Subjects: Abdominal obesity; classification and regression trees; external validation; dyslipidemia screening; positive and negative predictive values; sensitivity and specificity.
• Online Access: http://www.biomedcentral.com/1471-2288/4/7

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Methods</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>There were no striking differences between either the algebraic (i, ii) <it>vs</it>. non-algebraic (iii, iv), or the regression (i, iii) <it>vs</it>. classification (ii, iv) modeling approaches. Anticipated advantages of the CART <it>vs</it>. 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.</p>