Cardiovascular Risk Prediction

Cardiovascular disease (CVD) poses a major global health danger to human society.In order to identify high-risk individuals and implement early therapies, the application of machine learning techniques to predict the risk of CVD is highly relevant.Our study introduces the XGBH machine learning model for predicting cardiovascular disease (CVD) risk, incorporating data from 14,832 Chinese Shanxi CVD patients into the Kaggle dataset.By analyzing key variables such as age, systolic blood pressure, and cholesterol levels, our model offers a streamlined approach to early intervention with minimal accuracy loss.We also explored the Elastic Net model for predicting subclinical atherosclerosis (SA), integrating data from vascular ultrasonography and coronary artery calcification scores.Our findings highlight the importance of modifiable risk factors like hypertension and lifestyle choices in CVD risk assessment, emphasizing the potential of machine learning in optimizing risk prediction and improving patient outcomes.This study contributes to the growing body of research on utilizing machine learning techniques in healthcare, particularly in identifying highrisk individuals and implementing timely interventions.By leveraging advanced algorithms and comprehensive datasets, our model aims to enhance the accuracy and efficiency of CVD risk prediction, ultimately leading to better patient care and outcomes in the field of preventive cardiology.