Body Fat Percentage Prediction Using Intelligent Hybrid Approaches

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.

Diagnostic performance of an artificial neural network to predict excess body fat in children

“…The literature study revealed that attempted ANN approaches to predict BF% in adults showed partly contradictory results. [15][16][17][18] Barbosa et al evaluated the data of 79 adults. 15 was not convincingly better than linear regression.…”

“…13,14 Recently, some attempts to involve an artificial neural network (ANN) approach to predict BF% in adults have been reported. [15][16][17][18] Artificial intelligence has also been successfully used to predict childhood obesity after age two, using data collected prior to the second birthday by a clinical decision support system. 19 ANN approaches are commonly used to address complex, linear, and nonlinear relations.…”

“…Kupusinac et al evaluated data of 2755 adults, whose BF% was measured using bioelectrical impedance analysis 17. They compared the result of an ANN (input units: age, gender, BMI) to five different published formulas[41][42][43][44][45] and reported that the ANN increased average predictive accuracy for +1.23% to +3.12%.Shao evaluated data of 243 adults 18. It was not clear how BF% was measured.…”

Hybrid Soft Computing Schemes for the Prediction of Import Demand of Crude Oil in Taiwan

“…Recent research indicates that hybrid systems which are integrated with several standard ones can help to achieve a better performance for some applications. For example, the hybrid modeling applications have been reported in forecasting [57][58][59][60][61][62], credit risk [61], and manufacturing process [41][42][43][44]. As a consequence, this study proposes a two-step hybrid data mining mechanisms to predict the demand of ICO in Taiwan.…”

Recognition of Process Disturbances for an SPC/EPC Stochastic System Using Support Vector Machine and Artificial Neural Network Approaches

“…Due to their greater generalization ability and superior performance in practical applications [23][24][25][26][27][28][29], this study uses artificial neural network (ANN) and support vector machine (SVM) to serve as the classifiers to identify three commonly observed disturbance patterns [20,30,31]. Those three process disturbance patterns include shift, trend, and cycle patterns.…”