An Improved Back Propagation Neural Network Model and Its Application

Abstract

Stroke is one of the most serious disease, and the incidence rate of stroke is confirmed to be related to environmental factors including temperature, pressure and humidity .In order to obtain the relationship between the incidence rate and environmental factors , we research on local daily meteorological data and stroke disease cases from January 2008 to December 2012, which is provided by the administrative department of public health and medical institutions statistics in China, then build the improved BPNN(Back propagation neural network) model to carry out data analysis and processing, obtain the weight matrix between them. It can be seen that the relationship between incidence rate and pressure is the highest degree from the value of weight matrix, and pressure is positive correlation with the incidence rate. The relationship between the temperature and incidence rate is second, and they are negative correlation. The incidence between average relative humidity and correlation is quite small. The results show that the model can be used to predict the future stroke incidence rate under various meteorological conditions, and it can play a certain role in making disease knowledge popular and providing a reference to potential patients.