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The Scientific World Journal
Volume 2014, Article ID 383910, 8 pages
http://dx.doi.org/10.1155/2014/383910
Research Article

Body Fat Percentage Prediction Using Intelligent Hybrid Approaches

Department of Statistics and Information Science, Fu Jen Catholic University, 510, Chung-Cheng Road, Xinzhuang District, New Taipei City 24205, Taiwan

Received 24 December 2013; Accepted 15 January 2014; Published 2 March 2014

Academic Editors: D.-C. Lou and P. Melin

Copyright © 2014 Yuehjen E. Shao. 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.

Abstract

Excess of body fat often leads to obesity. Obesity is typically associated with serious medical diseases, such as cancer, heart disease, and diabetes. Accordingly, knowing the body fat is an extremely important issue since it affects everyone’s health. Although there are several ways to measure the body fat percentage (BFP), the accurate methods are often associated with hassle and/or high costs. Traditional single-stage approaches may use certain body measurements or explanatory variables to predict the BFP. Diverging from existing approaches, this study proposes new intelligent hybrid approaches to obtain fewer explanatory variables, and the proposed forecasting models are able to effectively predict the BFP. The proposed hybrid models consist of multiple regression (MR), artificial neural network (ANN), multivariate adaptive regression splines (MARS), and support vector regression (SVR) techniques. The first stage of the modeling includes the use of MR and MARS to obtain fewer but more important sets of explanatory variables. In the second stage, the remaining important variables are served as inputs for the other forecasting methods. A real dataset was used to demonstrate the development of the proposed hybrid models. The prediction results revealed that the proposed hybrid schemes outperformed the typical, single-stage forecasting models.