The Prediction of Human Abdominal Adiposity Based on the Combination of a Particle Swarm Algorithm and Support Vector Machine

Xiue Gao, Wenxue Xie, Shifeng Chen, Junjie Yang and Bo Chen
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Xiue Gao: College of Information Engineering, Lingnan Normal University, 29th Cunjin Road, Chikan Zone, Zhanjiang 524048, Guangdong, China
Wenxue Xie: College of Information Engineering, Lingnan Normal University, 29th Cunjin Road, Chikan Zone, Zhanjiang 524048, Guangdong, China
Shifeng Chen: College of Information Engineering, Lingnan Normal University, 29th Cunjin Road, Chikan Zone, Zhanjiang 524048, Guangdong, China
Junjie Yang: College of Information Engineering, Lingnan Normal University, 29th Cunjin Road, Chikan Zone, Zhanjiang 524048, Guangdong, China
Bo Chen: College of Information Engineering, Lingnan Normal University, 29th Cunjin Road, Chikan Zone, Zhanjiang 524048, Guangdong, China

IJERPH, 2020, vol. 17, issue 3, 1-10

Abstract: Background : Abdominal adiposity is an important risk factor of chronic cardiovascular diseases, thus the prediction of abdominal adiposity and obesity can reduce the risks of contracting such diseases. However, the current prediction models display low accuracy and high sample size dependence. The purpose of this study is to put forward a new prediction method based on an improved support vector machine (SVM) to solve these problems. Methods : A total of 200 individuals participated in this study and were further divided into a modeling group and a test group. Their physiological parameters (height, weight, age, the four parameters of abdominal impedance and body fat mass) were measured using the body composition tester (the universal INBODY measurement device) based on BIA. Intelligent algorithms were used in the modeling group to build predictive models and the test group was used in model performance evaluation. Firstly, the optimal boundary C and parameter gamma were optimized by the particle swarm algorithm. We then developed an algorithm to classify human abdominal adiposity according to the parameter setup of the SVM algorithm and constructed the prediction model using this algorithm. Finally, we designed experiments to compare the performances of the proposed method and the other methods. Results : There are different abdominal obesity prediction models in the 1 KHz and 250 KHz frequency bands. The experimental data demonstrates that for the frequency band of 250 KHz, the proposed method can reduce the false classification rate by 10.7%, 15%, and 33% in relation to the sole SVM algorithm, the regression model, and the waistline measurement model, respectively. For the frequency band of 1 KHz, the proposed model is still more accurate. (4) Conclusions : The proposed method effectively improves the prediction accuracy and reduces the sample size dependence of the algorithm, which can provide a reference for abdominal obesity.

Keywords: human abdominal adiposity; selection of characteristic parameters; particle swarm algorithm; improved support vector machine (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
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