• 主题
高级搜索  >
历史搜索 清空历史
首页> 美国卫生研究院文献>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
【2h】

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人体肥胖预测。

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务，本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文，但由于OA文献来源多样且变更频繁，仍可能出现获取不到、文献不完整或与标题不符等情况，如果获取不到我们将提供退款服务。请知悉。
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

: 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. : 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. : 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) : 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：：腹部肥胖是慢性心血管疾病的重要危险因素，因此预测腹部肥胖和肥胖可以降低患上此类疾病的风险。但是，当前的预测模型显示出较低的准确性和较高的样本量依赖性。这项研究的目的是提出一种基于改进的支持向量机（SVM）的新的预测方法来解决这些问题。 ：共有200个人参加了这项研究，并进一步分为建模组和测试组。使用基于BIA的人体成分测试仪（通用INBODY测量设备）测量其生理参数（身高，体重，年龄，腹部阻抗和脂肪含量的四个参数）。在建模组中使用智能算法来构建预测模型，而测试组则用于模型性能评估。首先，利用粒子群算法对最优边界C和参数γ进行了优化。然后，我们根据支持向量机算法的参数设置，开发了一种对人体腹部肥胖进行分类的算法，并使用该算法构建了预测模型。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。 ：在1 KHz和250 KHz频带中有不同的腹部肥胖预测模型。实验数据表明，相对于单独的SVM算法，回归模型和腰围测量模型，该方法在250 KHz频段上可以将错误分类率降低10.7％，15％和33％，分别。对于1 KHz的频带，所提出的模型仍然更加准确。 （4）：该方法有效地提高了预测的准确性，减少了算法的样本量依赖性，可为腹部肥胖提供参考。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号