Identifying cardiovascular disease risk in the U.S. population using environmental volatile organic compounds exposure: A machine learning predictive model based on the SHAP methodology
This study is the first to utilize VOC exposure data to construct an ML model for predicting CVD risk. The findings highlight the potential of combining environmental exposure data with demographic information to enhance CVD risk prediction, supporting the development of personalized prevention and intervention strategies.
Authors
- Yanze Wu
- Yunlei Xia
- Qingyun Yu
- Min Zhu (ORCID: https://orcid.org/0000-0002-0706-6390)
- Qingan Fu
- Xiaowei Ma (ORCID: https://orcid.org/0000-0001-8212-5453)
- Renqiang Yang (ORCID: https://orcid.org/0000-0001-6517-8860)
- Zhekang Liu
Institutions
- Nanchang University (CN)
- First Affiliated Hospital of Jiangxi Medical College (CN)
- Jiujiang First People's Hospital (CN)
- Second Affiliated Hospital of Nanchang University (CN)
Publication Details
- Journal
- Ecotoxicology and Environmental Safety
- Published
- 2024-10-23
- DOI
- https://doi.org/10.1016/j.ecoenv.2024.117210
- Citations
- 38
- Primary Topic
- Air Quality Monitoring and Forecasting
- Type
- article
- Field-Weighted Citation Impact
- 7.82
Funders
- National Natural Science Foundation of China