Research on Risk Prediction for Cardiovascular Diseases

Cardiovascular disease is a common disease that seriously threatens human health, and occupies the first place in the death caused by various diseases. Despite the fact that studies have documented many factors that lead to cardiovascular disease in medical development, there are still many unknown factors that have not been explored. To provide a comprehensive summary and explore various indicators for certain patients with cardiovascular diseases, a binary logistic regression model was used in this study. It has been concluded that age, Trest BPS, Cholestrol, FBS, Rest ECG, and slope have limited impact on the indicators, but gender, Exang, old peak, ca, and thal have some negative effects on them. It can be observed from the table above that the research model has an overall prediction accuracy of 85.15%, and its fit is acceptable. The precise and reliable materials given by these data can be utilized for future medical research, specifically in cardiovascular disease research, and they also give direction for subsequent research.