Predicting blood pressure from physiological index data using the SVR algorithm

Zhang, Bing; Ren, Huihui; Huang, Guoyan; Cheng, Yongqiang; Hu, Changzhen

Abstract

© 2019 The Author(s). 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. Results: This paper proposes an efficient blood pressure prediction method based on the support vector machine regression (SVR) algorithm to solve the key gap between the need for continuous measurement for prophylaxis and the lack of an effective method for continuous measurement. The results of the algorithm were compared with those obtained from two classical machine learning algorithms, i.e., linear regression (LinearR), back propagation neural network (BP), with respect to six evaluation indexes (accuracy, pass rate, mean absolute percentage error (MAPE), mean absolute error (MAE), R-squared coefficient of determination (R 2) and Spearman's rank correlation coefficient). The experimental results showed that the SVR model can accurately and effectively predict blood pressure. Conclusion: The multi-feature joint training and predicting techniques in machine learning can potentially complement and greatly improve the accuracy of traditional blood pressure measurement, resulting in better disease classification and more accurate clinical judgements.

Citation

Zhang, B., Ren, H., Huang, G., Cheng, Y., & Hu, C. (2019). Predicting blood pressure from physiological index data using the SVR algorithm. BMC Bioinformatics, 20(1), Article 109. https://doi.org/10.1186/s12859-019-2667-y

• Journal Article Type: Article
• Acceptance Date: Feb 1, 2019
• Online Publication Date: Feb 28, 2019
• Publication Date: Feb 28, 2019
• Deposit Date: Mar 1, 2019
• Publicly Available Date: Mar 4, 2019
• Journal: BMC Bioinformatics
• Print ISSN: 1471-2105
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 20
• Issue: 1
• Article Number: 109
• DOI: https://doi.org/10.1186/s12859-019-2667-y
• Keywords: Biochemistry; Applied Mathematics; Molecular Biology; Structural Biology; Computer Science Applications
• Public URL: https://hull-repository.worktribe.com/output/1346232
• Publisher URL: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-2667-y
• Additional Information: Received: 1 September 2018; Accepted: 1 February 2019; First Online: 28 February 2019; : The study was approved by the Department of Sport, Health and Exercise Science, the University of Hull Ethics Committee and all experimental procedures conformed to the Declaration of Helsinki. All participants provided written informed consent after having all experimental procedures explained to them both verbally and in writing.; : Not applicable.; : The authors declare no conflict of interest.; : Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
• Contract Date: Mar 4, 2019