计算机与现代化

• 算法设计与分析 • 上一篇    下一篇

基于网格搜索与交叉验证的SVR血压预测

  

  1. (贵州大学大数据与信息工程学院,贵州贵阳550025)
  • 收稿日期:2019-07-22 出版日期:2020-03-24 发布日期:2020-03-30
  • 作者简介:奚杏杏(1994-),女,山东巨野人,硕士研究生,研究方向:图像处理,大数据应用,E-mail: 951614549@qq.com; 刘宇红(1963-),男,教授,研究方向:嵌入式通信系统,云计算,大数据应用,E-mail: yhliu2@gzu.edu.cn; 通信作者:张荣芬(1977-),女,教授,研究方向:嵌入式系统,机器视觉,智能算法,大数据应用,E-mail: rfzhang@gzu.edu.cn。
  • 基金资助:
    贵州省科技计划项目(黔科合平台人才[2016]5707,黔科合基础[2019]1099)

SVR Blood Pressure Prediction Method Based on Grid Search and Cross Validation

  1. (College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China)
  • Received:2019-07-22 Online:2020-03-24 Published:2020-03-30

摘要: 针对现存血压测量方法不规范、波动范围大且预测准确率低下等问题,提出一种基于网格搜索与交叉验证相结合的支持向量回归(K-SVR)的血压预测算法。该算法首先对数据进行清洗,随后利用网格搜索与交叉验证相结合的方法寻找出最优参数对,然后通过分析人体生理指标数据心率、血氧与血压之间的隐含关系来建立相应的血压预测模型,最后将预测得到的结果与另外几种比较经典的机器学习模型得到的结果进行对比,并利用准确率及均方根误差这2种指标进行评估。实验结果表明,该算法对于高压和低压的预测准确率约为71.39%、81.69%,均方根误差值约为0.5349、0.4279,均明显优于传统的机器学习算法。

关键词: 生理指标数据, 网格搜索, 交叉验证, 血压预测, 支持向量回归

Abstract: Aiming at the problems of nonstandard blood pressure measurement, large fluctuation range and low prediction accuracy, a support vector regression (SVR) blood pressure prediction algorithm based on grid search with cross validation is proposed. The algorithm first cleans the data and then finds the optimal parameter pair by combining grid search and cross validation. Then, it establishes the corresponding blood pressure prediction model by analyzing the implicit relationship between heart rate, blood oxygen and blood pressure of human physiological index data. Finally, the predicted results are compared with other several kinds of blood pressure prediction models. The results of classical machine learning model are compared, and the accuracy and rootmean square error are used to evaluate. The experimental results show that the prediction accuracy of this algorithm is about 71.39% and 81.69% respectively for high and low pressures, and the root mean square error is about 0.5349 and 0.4279, which are obviously superior to the traditional machine learning algorithms.

Key words: physiological index data, grid search, cross validation, blood pressure prediction, SVR

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