Predicting blood pressure from physiological index data using the SVR algorithm

• DOI: 10.1186/s12859-019-2667-y
• Publication Date: 2019-02-28T09:04:17Z
• Authors: Bing Zhang; Huihui Ren; Guoyan Huang; Yongqiang Cheng; Changzhen Hu
• Keywords: Adult; Male; SVR; Support Vector Machine; QH301-705.5; Computer applications to medicine. Medical informatics; R858-859.7; 610; Blood Pressure; 02 engineering and technology; Biochemistry; Machine Learning; Structural Biology; Blood pressure prediction; 0202 electrical engineering, electronic engineering, information engineering; Humans; Biology (General); Molecular Biology; Physiological index data; Applied Mathematics; Computer Science Applications; 004; 3. Good health; Linear Models; Female; Neural Networks, Computer; Research Article

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.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 (R2) and Spearman's rank correlation coefficient). The experimental results showed that the SVR model can accurately and effectively predict blood pressure.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.