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, 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, Yan Yan and Rong Chen contributed equally to this study.

Review of AlexNet for Medical Image Classification

“…Revival with Backpropagation: In the 1980s, the field of neural networks experienced a resurgence with the development of the backpropagation (BP) algorithm, which allowed for efficient training of multi-layer neural networks [2]. Researchers such as Geoffrey Hinton and Yann LeCun contributed significantly during this period.…”

“…BP neural networks affect complex model fitting and distribution approximation that traditional statistical methods cannot achieve. It has excellent potential for development compared to many machine learning algorithms [2]. It has also facilitated many innovations in the field of DL, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNN) [9].…”

“…where 𝜇𝜇 𝐵𝐵 and 𝜎𝜎 𝐵𝐵 2 are the mean and variance of 𝐵𝐵 , respectively. Then, normalizing 𝑥𝑥 𝑖𝑖 using mean 𝜇𝜇 𝐵𝐵 and variance 𝜎𝜎 𝐵𝐵 2 , namely…”