Using interpretable machine learning methods to identify the relative importance of lifestyle factors for overweight and obesity in adults: pooled evidence from CHNS and NHANES.

Journal: BMC public health
PMID: 39487401

Abstract

BACKGROUND: Overweight and obesity pose a huge burden on individuals and society. While the relationship between lifestyle factors and overweight and obesity is well-established, the relative contribution of specific lifestyle factors remains unclear. To address this gap in the literature, this study utilizes interpretable machine learning methods to identify the relative importance of specific lifestyle factors as predictors of overweight and obesity in adults.

Authors

  • Zhiyuan Sun
    Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China.
  • Yunhao Yuan
    School of Computer Science and Technology, Yangzhou University, Yangzhou, Jiangsu, 225127, China.
  • Vahid Farrahi
    Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland. Electronic address: Vahid.farrahi@oulu.fi.
  • Fabian Herold
    Research Group Degenerative and Chronic Diseases, Movement, Faculty of Health Sciences Brandenburg, University of Potsdam, 14476, Potsdam, Germany.
  • Zhengwang Xia
  • Xuan Xiong
    Department of Physical Education, Nanjing University, Nanjing, 210033, China.
  • Zhiyuan Qiao
    College of Physical Education, Yangzhou University, Yangzhou, 225127, China.
  • Yifan Shi
    Department of Mechanical and Materials Engineering, Queen's University, 130 Stuart Street, Kingston, ON K7L 3N6, Canada.
  • Yahui Yang
    College of Physical Education, Yangzhou University, Yangzhou, 225127, China.
  • Kai Qi
    College of Physical Education, Yangzhou University, Yangzhou, 225127, China.
  • Yufei Liu
    China Agricultural University, Beijing 100083, China.
  • Decheng Xu
    College of Physical Education, Yangzhou University, Yangzhou, 225127, China.
  • Liye Zou
    Body-Brain-Mind Laboratory, School of Psychology, Shenzhen University, Shenzhen, 518060, China. liyezou123@gmail.com.
  • Aiguo Chen
    College of Physical Education, Yangzhou University, Yangzhou, 225127, China. agchen@nsi.edu.cn.

Keywords

External Resources

Popular Topics
Recent Journals