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The risk of cardiovascular disease (CVD) is a serious health threat to human society worldwide. The use of machine learning methods to predict the risk of CVD is of great relevance to identify high-risk patients and take timely interventions. In this study, we propose the XGBH machine learning model, which is a CVD risk prediction model based on key contributing features. In this paper, the generalisation of the model was enhanced by adding retrospective data of 14,832 Chinese Shanxi CVD patients to the kaggle dataset. The XGBH risk prediction model proposed in this paper was validated to be highly accurate (AUC = 0.81) compared to the baseline risk score (AUC = 0.65), and the accuracy of the model for CVD risk prediction was improved with the inclusion of the conventional biometric BMI variable. To increase the clinical application of the model, a simpler diagnostic model was designed in this paper, which requires only three characteristics from the patient (age, value of systolic blood pressure and whether cholesterol is normal or not) to enable early intervention in the treatment of high-risk patients with a slight reduction in accuracy (AUC = 0.79). Ultimately, a CVD risk score model with few features and high accuracy will be established based on the main contributing features. Of course, further prospective studies, as well as studies with other populations, are needed to assess the actual clinical effectiveness of the XGBH risk prediction model. [ABSTRACT FROM AUTHOR]

Cardiovascular disease (CVD) risk prediction shows great significance for disease diagnosis and treatment, especially early intervention for CVD, which has a direct impact on preventing and reducing adverse outcomes. In this paper, we collected clinical indicators and outcomes of 14,832 patients with cardiovascular disease in Shanxi, China, and proposed a cardiovascular disease risk prediction model, XGBH, based on key contributing characteristics to perform risk scoring of patients' clinical outcomes. The XGBH risk prediction model had high accuracy, with a significant improvement compared to the baseline risk score (AUC = 0.80 vs. AUC = 0.65). At the same time, we found that with the addition of conventional biometric variables, the accuracy of the model's CVD risk prediction would also be improved. Finally, we designed a simpler model to quantify disease risk based on only three questions answered by the patient, with only a modest reduction in accuracy (AUC = 0.79), and providing a valid risk assessment for CVD. Overall, our models may allow early-stage intervention in high-risk patients, as well as a cost-effective screening approach. Further prospective studies and studies in other populations are needed to assess the actual clinical effect of XGBH risk prediction models. [ABSTRACT FROM AUTHOR]