Preface to First Edition - lib

CHAPTER 9Recursive Partitioning: PredictingBody Fat and Glaucoma Diagnosis9.1 IntroductionWorldwide, overweight and obesity are considered to be major health problemsbecause of their strong association with a higher risk of diseases of themetabolic syndrome, including diabetes mellitus and cardiovascular disease, aswell as with certain forms of cancer. Obesity is frequently evaluated by usingsimple indicators such as body mass index, waist circumference, or waist-tohipratio. Specificity and adequacy of these indicators are still controversial,mainly because they do not allow a precise assessment of body composition.Body fat, especially visceral fat, is suggested to be a better predictor of diseasesof the metabolic syndrome. Garcia et al. (2005) report on the developmentof a multiple linear regression model for body fat content by meansof p = 9 common anthropometric measurements which were obtained forn = 71 healthy German women. In addition, the women’s body compositionwas measured by Dual Energy X-Ray Absorptiometry (DXA). This referencemethod is very accurate in measuring body fat but finds little applicability inpractical environments, mainly because of high costs and the methodologicalefforts needed. Therefore, a simple regression model for predicting DXA measurementsof body fat is of special interest for the practitioner. The followingvariables are available (the measurements are given in Table 9.1):DEXfat: body fat measured by DXA, the response variable,age: age of the subject in years,waistcirc: waist circumference,hipcirc: hip circumference,elbowbreadth: breadth of the elbow, andkneebreadth: breadth of the knee.Table 9.1:bodyfat data (package mboost). Body fat predictionby skinfold thickness, circumferences, and bonebreadths.DEXfat age waistcirc hipcirc elbowbreadth kneebreadth41.68 57 100.0 112.0 7.1 9.443.29 65 99.5 116.5 6.5 8.9161© 2010 by Taylor and Francis Group, LLC