Intelligent feature subset selection with unspecified number for body fat prediction based on binary-GA and Fuzzy-Binary-GA

Abstract

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. Therefore, certain measurements or explanatory variables are used to predict the BFP. This study proposes an intelligent feature subset selection approach with unspecified number of features based on Binary GA and Fuzzy Binary GA algorithms to discover most important variable or feature and facilitate an artificial neural network (ANN) classifier model which is applied for body fat prediction (BFP). The proposed forecasting model is able to effectively predict the BFP with error of ± 3.64031% and the most effective feature of forearm circumference among total twelve features by using Fuzzy Binary GA.