Prediction and Analysis of Cerebrovascular Disease Mortality Based on Grey Model

This paper aims to explore the optimum modelling data for predicting cerebrovascular disease mortality in China by using GM(1,1) model. Also, it will discuss the applicability of grey relational analysis to analyze the related factors of cerebrovascular disease mortality. The subsequence sets with different data lengths were used to establish rolling GM(1,1), and predictive efficiency was judged by prediction error. Grey relational analysis was used to analyze the interrelationships between population, health expenses, environmental pollution factors and mortality. Results show that GM (1,1) grey prediction model can well predict the mortality of cerebrovascular diseases in China, especially when the modelling data is 8, the overall prediction effect is the best. In recent years, the prediction effect of 4 or 5 data modelling is better than that of more data modelling. Grey relational analysis suggests that population aging has a greater impact on the mortality of cerebrovascular diseases than other risk factors. The grey system theory is suitable for the epidemiological study of cerebrovascular diseases.