Machine learning and Mendelian randomization identify key lifestyle factors in coronary heart disease: A NHANES-Based study
The SVM machine learning model, based on NHANES data, enables faster and more efficient prediction of CHD. The study identified age, sex, BMI, TC intake, sleep duration, diastolic blood pressure, and smoking as the lifestyle variables with the greatest impact on CHD. This dual approach advances precision prevention by combining predictive accuracy with genetic evidence.
Authors
- Jing Luo (ORCID: https://orcid.org/0000-0002-2454-2168)
- Xue W. Mei (ORCID: https://orcid.org/0000-0002-6279-4884)
- Xiangmei Yang
- Fenglin Wu (ORCID: https://orcid.org/0000-0001-8287-4585)
- Rongchuan Yue
- Lijuan Xiong (ORCID: https://orcid.org/0000-0001-5643-4244)
- Peng Zhou (ORCID: https://orcid.org/0000-0001-9863-4201)
- Shikang Li (ORCID: https://orcid.org/0000-0002-8187-3676)
- Yonghong Zhang (ORCID: https://orcid.org/0000-0002-7740-9408)
- Yang Cui (ORCID: https://orcid.org/0000-0002-4732-8877)
- Lang Zeng
- Yijuan Huang
Institutions
- North Sichuan Medical University (CN)
- Affiliated Hospital of North Sichuan Medical College (CN)
- Guang’anmen Hospital (CN)
Publication Details
- Journal
- International Journal of Cardiology Cardiovascular Risk and Prevention
- Published
- 2025-10-26
- DOI
- https://doi.org/10.1016/j.ijcrp.2025.200536
- Primary Topic
- Cardiovascular Health and Risk Factors
- Type
- article
- Field-Weighted Citation Impact
- 0.00