Abstract
The BP neural network was optimized by particle swarm optimization algorithm (PSO), and the PSO-BP neural network model was constructed. The prediction effect of the model was evaluated comprehensively by comparing it with BP neural network model and Logistic regression model. Based on PSO-BP model, the mean impact value algorithm (MIV) was used to screen the risk factors of hypertension, and the disease risk prediction model was established. In the evaluation of fitting effect, the root mean square error and determination coefficient of PSO-BP neural network are 0.09 and 0.29, respectively. In the prediction performance comparison, the accuracy, sensitivity, specificity and area under the ROC curve of PSO-BP neural network were 85.38%, 43.90%, 96.66% and 0.86, respectively. The results show that the BP neural network optimized by particle swarm optimization has the best fitting effect and prediction performance. The MIV algorithm can screen out the risk factors related to hypertension, and then construct the disease prediction model, which can provide a new idea for the analysis of hypertension.