Predicting blood pressure from physiological index data using the SVR algorithm

BMC Bioinformatics
Bing ZhangChangzhen Hu

Abstract

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...Continue Reading

References

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Citations

Dec 31, 2019·International Journal for Numerical Methods in Biomedical Engineering·Janne M J HuttunenHarri Lindholm
Jul 19, 2020·Clinical Journal of the American Society of Nephrology : CJASN·Jay A PanditDaniel Batlle
Jun 5, 2021·Medical & Biological Engineering & Computing·Nishigandha Dnyaneshwar Agham, Uttam M Chaskar

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Methods Mentioned

BETA
feature extraction

Software Mentioned

LinearR
SVR
Windows

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