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Application of Combined Prediction Model Based on Core and Coritivity Theory in Continuous Blood Pressure Prediction

Author(s):

Kai Zhou, Zhixiang Yin*, Fei Guo and Jiasi Li  

Abstract:


Background and Objective: Blood pressure is vital evidence for clinicians to predict diseases and check the curative effect of diagnosis and treatment. To further improve the prediction accuracy of blood pressure, this paper proposes a combined prediction model of blood pressure based on coritivity theory and photoplethysmography.

Method: First of all, we extract eight features of photoplethysmogram, followed by using eight machine learning prediction algorithms such as K-nearest neighbor, classification and regression trees and random forest to predict systolic blood pressure. Secondly, aiming at the problem of sub-model selection of combination forecasting model, from the point of graph theory, we construct an undirected network graph G, the results of each single prediction model constitute a vertex set. If the maximum mutual information coefficient between vertices is greater than or equal to 0.69, the vertices are connected by edges. The maximum core of graph G is a submodel of the combinatorial model.

Results: According to the definition of core and coritivity, the maximum core of G is random forest regression and Gaussian kernel support vector regression model. The results show that the SDP estimation error of the combined prediction model based on random forest regression and Gaussian kernel support vector regression is 3.56 ±5.28mmhg, which is better than other single models and meets the AAMI standards.

Conclusion: The combined model determined by core and coritivity has higher prediction performance for blood pressure.

Keywords:

Blood pressure, Machine learning, Photoplethysmography, Combination prediction, Core and coritivity, maximum mutual information coefficient

Affiliation:

School of Mathematics Physics and Statistics, Shanghai University of Engineering Science, Shanghai 201620, School of Mathematics Physics and Statistics, Shanghai University of Engineering Science, Shanghai 201620, School of Mathematics Physics and Statistics, Shanghai University of Engineering Science, Shanghai 201620, School of Mathematics Physics and Statistics, Shanghai University of Engineering Science, Shanghai 201620



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