Research on Prediction Models of Cardiovascular Diseases Based on Artificial Neural Networks

With the rapid development of the social economy, significant improvement in the living standards of residents, changes in lifestyle, and the increasingly severe issue of population aging, cardiovascular diseases have become one of the most widespread diseases, posing a serious threat to public health. In order to explore the inherent patterns of the occurrence and development of these diseases, it is necessary to use quantitative methods to describe the correlation between risk factors and disease incidence, as well as to predict the epidemic trends of the disease. This will provide scientific theoretical support for medical professionals and public health prevention agencies, enabling the implementation of effective preventive and control measures. In the field of dynamic prediction of non-stationary disease incidence, various methods have been proposed, each with its own advantages and disadvantages. Artificial neural network technology, particularly the classic BP neural network, has shown significant advantages in handling nonlinear pattern recognition and prediction tasks. Therefore, this study constructs a disease incidence prediction model based on the BP neural network and verifies the models effectiveness through the analysis of practical cases.