An Empirical Study on Predicting Blood Pressure Using Classification and Regression Trees

Blood pressure (BP) diseases have become one of the major threats to human health. Continuous measurement of BP 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, noninvasive BP measurement is a promising use for continuous measurement. Thus in this paper, classification and regression trees (CARTs) are proposed and applied to tackle the problem. First, according to the characteristics of different information, different CART models are constructed. Second, 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, and Theil inequality coefficient, and the required training time is also comparatively less. With increasing data, the accuracy rate of predicting systolic BP and diastolic BP by CART exceeds 90%, and the training time is less than 0.5 s.