An Empirical study on Predicting Blood Pressure using Classification and Regression Trees

Zhang, Bing; Wei, Zhiyao; Ren, Jiadong; Cheng, Yongqiang; Zheng, Zhangqi

Abstract

Blood pressure diseases have become one of the major threats to human health. Continuous measurement of blood
pressure has proven to be a prerequisite for effective incident prevention. In contrast with the traditional prediction models with low
measurement accuracy or long training time, non-invasive blood pressure measurement is a promising use for continuous
measurement. Thus in this paper, classification and regression trees (CART) are proposed and applied to tackle the problem. Firstly,
according to the characteristics of different information, different CART models are constructed. Secondly, in order to avoid the
over-fitting problem of these models, the cross-validation method is used for selecting the optimum parameters so as to achieve the
best generalization of these models. Based on the biological data collected from CM400 monitor, this approach has achieved better
performance than the common existing models such as linear regression, ridge regression, the support vector machine and neural
network in terms of accuracy rate, root mean square error, deviation rate, Theil IC, and the required training time is also comparatively
less. With increasing data, the accuracy rate of predicting systolic blood pressure and diastolic blood pressure by CART exceeds 90%,
and the training time is less than 0.5s.

Citation

Zhang, B., Wei, Z., Ren, J., Cheng, Y., & Zheng, Z. (2018). An Empirical study on Predicting Blood Pressure using Classification and Regression Trees. IEEE Access, 6, 21758 - 21768. https://doi.org/10.1109/access.2017.2787980

• Acceptance Date: Dec 23, 2017
• Publication Date: Jan 10, 2018
• Deposit Date: Feb 26, 2018
• Publicly Available Date: Feb 27, 2018
• Journal: IEEE Access
• Electronic ISSN: 2169-3536
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 6
• Pages: 21758 - 21768
• DOI: https://doi.org/10.1109/access.2017.2787980
• Keywords: General Engineering; General Materials Science; General Computer Science
• Public URL: https://hull-repository.worktribe.com/output/669474
• Publisher URL: http://ieeexplore.ieee.org/document/8253438/
• Contract Date: Feb 27, 2018