Classification of Abdominal Visceral and Subcutaneous Fat Distributions by Body Shape Descriptors
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This study aims to explore new categorization that characterizes the distribution clusters of visceral and subcutaneous adipose tissues (VAT and SAT) measured by magnetic resonance imaging (MRI); to analyze the relationship between the VAT-SAT distribution patterns and the novel body shape descriptors (BSDs); and to develop a classifier to predict the fat distribution clusters using the BSDs. 66 male and 54 female participants were scanned by magnetic resonance imaging (MRI) and a stereovision body imaging (SBI) to measure participants’ abdominal VAT and SAT volumes and the BSDs. A fuzzy c-means algorithm was used to form the inherent grouping clusters of abdominal fat distributions. A support-vector-machine (SVM) classifier, with an embedded feature selection scheme, was employed to determine an optimal subset of the BSDs for predicting internal fat distributions. A five-fold cross-validation procedure was used to prevent over-fitting in the classification. The classification results of the BSDs were compared with those of the traditional anthropometric measurements and the Dual Energy X-Ray Absorptiometry (DXA) measurements. Four clusters were identified for abdominal fat distributions: low VAT and SAT, elevated VAT and SAT, higher SAT, and higher VAT. The cross-validation accuracies of the traditional anthropometric, DXA and BSD measurements are 85.0%, 87.5% and 90%, respectively.