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

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College of Information Engineering, Lingnan Normal University, 29th Cunjin Road, Chikan Zone, Zhanjiang 524048, Guangdong, China
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College of Information Engineering, Dalian University, 10th Xuefu street, Dalian Economic and Technological Development Zone, Dalian 116622, Liaoning, China
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Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(3), 1117; https://doi.org/10.3390/ijerph17031117
Received: 29 November 2019 / Revised: 30 January 2020 / Accepted: 5 February 2020 / Published: 10 February 2020
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. View Full-Text
Keywords: human abdominal adiposity; selection of characteristic parameters; particle swarm algorithm; improved support vector machine human abdominal adiposity; selection of characteristic parameters; particle swarm algorithm; improved support vector machine
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Gao, X.; Xie, W.; Chen, S.; Yang, J.; Chen, B. The Prediction of Human Abdominal Adiposity Based on the Combination of a Particle Swarm Algorithm and Support Vector Machine. Int. J. Environ. Res. Public Health 2020, 17, 1117.

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