ArticleScientific reports2023
Prediction of cardiovascular disease risk based on major contributing features.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 31 citations in OpenAlex.
- A scoping review on artificial intelligence-based tools for cardiovascular disease risk prediction.BMC medical informatics and decision making · 2026Article
- Machine-learning-based cardiovascular mortality prediction using a cumulative PMScientific reports · 2026Observational
- Machine learning used to study risk factors for chronic diseases: A scoping review.Canadian journal of public health = Revue canadienne de sante publique · 2026Article
- Applications of artificial intelligence in cardiovascular risk detection and prediction among adults with obesity: a scoping review.Frontiers in cardiovascular medicine · 2026Review
- Web-based cardiovascular disease risk prediction using machine learning.Frontiers in artificial intelligence · 2026Article
- OptiStack classifier: optimized stacking framework with ensemble feature engineering for enhanced cardiovascular risk prediction.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2025Article
- Prediction of cardiovascular disease based on multiple feature selection and improved PSO-XGBoost model.Scientific reports · 2025Article
- Multi-layered metabolic effects of trehalose on the liver proteome in apoE-knockout mice model of liver steatosis.Pharmacological reports : PR · 2024Article
- Enhancing heart disease prediction using a self-attention-based transformer model.Scientific reports · 2024Article
- Assessing the relative importance of vitamin D deficiency in cardiovascular health.Frontiers in cardiovascular medicine · 2024Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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