Application of back propagation neural network model optimized by particle swarm algorithm in predicting the risk of hypertension. Issue 12 (15th November 2022)

• Record Type: Journal Article
• Authors: Yan, Yan; Chen, Rong; Yang, Zihua; Ma, Yong; Huang, Jialu; Luo, Ling; Liu, Hao; Xu, Jian; Chen, Weiying; Ding, Yuanlin; Kong, Danli; Zhang, Qiaoli; Yu, Haibing
• Is Part Of: Journal of clinical hypertension. Volume 24:Issue 12(2022)
• Page Start: 1606
• Page End: 1617
• Publication Date: 2022-11-15
• Subjects: back propagation neural network -- hypertension -- logistic regression -- mean impact value -- particle swarm algorithm optimization; Hypertension -- Periodicals; 616.132
• DOI: 10.1111/jch.14597
• Languages: English
• ISSNs: 1524-6175

Abstract

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, respectively. In the comparison of prediction performance, the accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve of the back propagation neural network based on PSO algorithm were 85.38%, 43.90%, 96.66%, and 0.86, respectively. The results showed that the backpropagation neural network optimized by PSO had the best fitting effect and prediction performance. Meanwhile, the mean impact value algorithm could screen out the risk factors related to hypertension and build a disease prediction model, which can provide clues for exploring the pathogenesis of hypertension and preventing hypertension.