Application of back propagation neural network model optimized by particle swarm algorithm in predicting the risk of hypertension.

ABSTRACT: The structure of a back propagation neural network was optimized by a particle swarm optimization (PSO) algorithm, and a back propagation neural network model based on a PSO algorithm was constructed. By comparison with a general back propagation neural network and logistic regression, the fitting performance and prediction performance of the PSO algorithm is discussed. Furthermore, based on the back propagation neural network optimized by the PSO algorithm, the risk factors related to hypertension were further explored through the mean influence value algorithm to construct a risk prediction model. In the evaluation of the fitting effect, the root mean square error and coefficient of determination of the back propagation neural network based on the PSO algorithm were 0.09 and 0.29, respec

SUBMITTER: Yan Y 

PROVIDER: S-EPMC9731601 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature