Predicting blood pressure from physiological index data using the SVR algorithm.

Journal: BMC bioinformatics
Published Date:

Abstract

BACKGROUND: Blood pressure diseases have increasingly been identified as among the main factors threatening human health. How to accurately and conveniently measure blood pressure is the key to the implementation of effective prevention and control measures for blood pressure diseases. Traditional blood pressure measurement methods exhibit many inherent disadvantages, for example, the time needed for each measurement is difficult to determine, continuous measurement causes discomfort, and the measurement process is relatively cumbersome. Wearable devices that enable continuous measurement of blood pressure provide new opportunities and hopes. Although machine learning methods for blood pressure prediction have been studied, the accuracy of the results does not satisfy the needs of practical applications.

Authors

  • Bing Zhang
    School of Information Science and Engineering, Yanshan University, Hebei Avenue, Qinhuangdao, 066004, China.
  • Huihui Ren
    School of Information Science and Engineering, Yanshan University, Hebei Avenue, Qinhuangdao, 066004, China.
  • Guoyan Huang
    School of Information Science and Engineering, Yanshan University, Hebei Avenue, Qinhuangdao, 066004, China. hgy@ysu.edu.cn.
  • Yongqiang Cheng
    School of Electronic Science, National University of Defense Technology, Changsha 410073, China. yqcheng@nudt.edu.cn.
  • Changzhen Hu
    Beijing Key Laboratory of Software Security Engineering Technique, Beijing Institute of Technology, Beijing, 100081, China.