Prediction model of BP neural network based on improved genetic algorithm optimization for infectious diseases

Abstract

In recent years, with the high frequency of the infectious diseases outbreak, the prediction of the infectious diseases has become more and more important, so effective prediction of the infectious diseases can safeguard social stability and promote national economic prosperity. In order to improve the predictive accuracy of infectious diseases, the weight and threshold of BP neural network was optimized by using the improved genetic algorithm based on the PSO (particle swarm optimization algorithm) while the error function is the mean square error, the mean absolute error and the mean absolute percentage error. The simulation experimental results show that the optimized BP neural network can effectively reduce the mean square error, the mean absolute error and the mean absolute percentage error, and improve the prediction accuracy.