A Cardiovascular Disease Risk Score Model Based on High Contribution Characteristics

Abstract

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.

Keywords

• cardiovascular disease
• machine learning
• risk score