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.

Journal: Ecotoxicology and environmental safety
PMID: 39447292

Abstract

BACKGROUND: Cardiovascular disease (CVD) remains a leading cause of mortality globally. Environmental pollutants, specifically volatile organic compounds (VOCs), have been identified as significant risk factors. This study aims to develop a machine learning (ML) model to predict CVD risk based on VOC exposure and demographic data using SHapley Additive exPlanations (SHAP) for interpretability.

Authors

  • Qingan Fu
    Cardiovascular medicine department, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330006, China.
  • Yanze Wu
    School of Chemistry and Chemical Engineering, Materials Genome Initiative Center, and Key Laboratory of Scientific and Engineering Computing of Ministry of Education , Shanghai Jiao Tong University , Shanghai , China 200240.
  • Min Zhu
    Department of Infectious Diseases, Affiliated Taizhou Hospital of Wenzhou Medical University, No.50 Ximeng Road, Taizhou, 317000, China.
  • Yunlei Xia
    Cardiovascular medicine department, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330006, China.
  • Qingyun Yu
    Cardiovascular medicine department, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330006, China.
  • Zhekang Liu
    Rheumatology and immunology department, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330006, China.
  • Xiaowei Ma
    Cardiovascular medicine department, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330006, China.
  • Renqiang Yang
    Cardiovascular medicine department, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330006, China. Electronic address: Yang.RQ@ncu.edu.cn.

Keywords

External Resources

Popular Topics
Recent Journals