燕山大学学报2017，Vol.41Issue(5)：438-443,6.DOI:10.3969/j.issn.1007-791X.2017.05.009
基于支持向量回归的人体血压预测方法
Predicting method for human blood pressure based on SVR algorithm
摘要
Abstract
In view of the problems of long timing for take measurement, the harm causing by continuous measurement to the body and the cumbersome measurement process, an efficient and convenient blood pressure prediction algorithm based on support vector machine regression algorithm was proposed.It firstly analyzed the implicit relationship between human physiological index data and blood pressure and then established the SVR Model. The results of the algorithm were compared with those obtained from three classical machine learning algorithms, i. e. linear regression model, ridge regression model, and neural network model, against two evaluation indexes ( accuracy, root mean square error) . The experimental results showed that support vector machine regression model ( SVR) can accurately and effectively predict blood pressure and be superior to other algorithms.关键词
生理指标数据/支持向量回归算法/血压预测Key words
physiological index data/support vector machine regression/blood pressure prediction分类
信息技术与安全科学引用本文复制引用
赵谞博,赫英迪,李信政,任蓉,任家东..基于支持向量回归的人体血压预测方法[J].燕山大学学报,2017,41(5):438-443,6.基金项目
国家自然科学基金资助项目(61572420) （61572420）
