## 1 Introduction  
Cohort research has great advantages in revealing the risks and trends of chronic diseases. Cohort research has been paid more and more attention by medical researchers all over the world. China has more than 60 years of experience in cohort research, and has achieved a series of original research results. However, due to a late start, follow-up methods are lagging behind the other countries or regions. The current population cohort data in China are relatively small, mainly clinical trial data, and the range of data collection is relatively narrow; such cohort data has caused a certain bottleneck in the universality of medicine. At the same time, because of individual differences, it is difficult to use foreign cohort data as research data to study medical problems involving Chinese ethnic groups. The data used in this paper are from a large prospective Chinese chronic disease research project, (China Kadoorie Biobank, CKB [1; 2]), initiated by the China Centers for Disease Control and Prevention in collaboration with the University of Oxford, UK. The data is the largest in the Chinese cohort study, the most widely related data (including age, occupation, region, environment, etc. ), and the most reliable sample of local cohort data collected. It can provide basic data support for the study of etiology, disease trend and disease prediction of chronic diseases represented by circulatory system. Chronic diseases represented by circulatory diseases are characterized by high morbidity, high mortality, complicated etiology, long course of disease, great health damage, serious harm to the state and society, and the economic burden of treating and curing chronic diseases. It is very heavy for individual families, society and the country. In addition, the degree of aging in China is increasing, and the development trend of chronic diseases is gradually accelerating, which brings great challenges to China's medical and health care services. Therefore, the study of the etiology, pathogenesis and disease prediction of chronic diseases represented by the circulatory system is of great significance for disease intervention and control. We conducted circulatory disease prediction studies based on data from China's chronic disease prospective research project, to improve traditional algorithms used in medical research, and to use artificial intelligence machine learning algorithms to make the research algorithms better applicable to population cohorts. The field of disease prediction is to promote the discovery of new outcomes for more chronic disease predictions. We obtained the correlation between exposure factors and outcomes in Chinese chronic disease prospective research project data based on the single factor analysis model in a population cohort study and survival analysis. We evaluated and predicted the disease outcome of the circulation system based on Cox regression prediction model and support vector regression prediction model respectively, and the Cox regression model traditionally used in medical research was optimized based on the contraction prediction theory. The PSO algorithm based on the group intelligence theory optimized the parameters of the support vector regression prediction model. Then, the multi-model was performed. The prediction results and effects are compared, and the advantages and disadvantages of various prediction models are discussed. Finally, according to the analysis, the model usage scenarios and further optimization schemes are given. The main research contents of the thesis are summarized as follows: (a) Analysis of the data characteristics of China's chronic disease prospective research project. According to Cox proportional hazard theory and SVM theory, we constructed the Cox-CSDPC disease prediction model and SVM-CSDPC disease prediction model respectively; (b) By analyzing the results of Cox-CSDPC disease prediction model, a new prediction index is proposed.