Influencing Factors and Prediction of Incidence of Coronary Heart Disease

Coronary heart disease (CHD) is a disease of myocardial ischemia and hypoxia caused by coronary atherosclerosis, which is one of the main causes of death. This article selects relevant data from the Kaggle dataset and conducts rigorous preprocessing, such as data cleaning and outlier handling. Then, the classic Logistic regression model and random forest model were selected to conduct a comprehensive and in-depth analysis of many potential factors affecting the incidence of coronary heart disease. The results showed that age, BMI, education level, total cholesterol, blood pressure, diabetes, and lifestyle (smoking, drinking) have significant effects on the incidence of CHD. Then, the logistic regression model was used to predict the incidence rate of CHD. In addition, the numerical results of the OC-ROC Curve showed that the logistic regression model had good predictive performance. Finally, based on the research results, a series of targeted measures are proposed to enhance people's awareness of preventing CHD, effectively reduce medical expenditure, and improve the quality of life of the public.