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首页> 外文期刊>International Journal of Environmental Research and Public Health >The Prediction of Human Abdominal Adiposity Based on the Combination of a Particle Swarm Algorithm and Support Vector Machine
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The Prediction of Human Abdominal Adiposity Based on the Combination of a Particle Swarm Algorithm and Support Vector Machine

机译：基于粒子群算法和支持向量机的组合的人腹部肥胖预测

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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.
机译：背景：腹部肥胖是慢性心血管疾病的重要危险因素，因此腹部肥胖和肥胖的预测可以降低​​收缩这些疾病的风险。然而，当前预测模型显示低精度和高样本尺寸依赖性。本研究的目的是基于改进的支持向量机（SVM）提出了一种新的预测方法来解决这些问题。方法：本研究共有200人参加，进一步分为造型组和试验组。使用基于BIA的身体成分测试仪（通用体积测量装置）测量它们的生理参数（高度，体重，年龄，腹部阻抗和体脂质量的四个参数）。在建模组中使用智能算法来构建预测模型，测试组用于模型性能评估。首先，通过粒子群算法优化最佳边界C和参数伽马。然后，我们开发了一种算法根据SVM算法的参数设置对人类腹部肥胖进行分类，并使用该算法构建预测模型。最后，我们设计了实验以比较所提出的方法和其他方法的性能。结果：1 kHz和250 kHz频段有不同的腹部肥胖预测模型。实验数据表明，对于250 kHz的频带，所提出的方法可以将错误分类率降低10.7％，15％和33％，而唯一的SVM算法，回归模型和腰线测量模型分别。对于1 kHz的频带，所提出的模型仍然更准确。 （4）结论：提出的方法有效提高了预测精度，降低了算法的样本尺寸依赖性，这可以为腹部肥胖提供参考。

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