Study of obesity research using machine learning methods: A bibliometric and visualization analysis from 2004 to 2023.

Journal: Medicine
PMID:

Abstract

BACKGROUND: Obesity, a multifactorial and complex health condition, has emerged as a significant global public health concern. Integrating machine learning techniques into obesity research offers great promise as an interdisciplinary field, particularly in the screening, diagnosis, and analysis of obesity. Nevertheless, the publications on using machine learning methods in obesity research have not been systematically evaluated. Hence, this study aimed to quantitatively examine, visualize, and analyze the publications concerning the use of machine learning methods in obesity research by means of bibliometrics.

Authors

  • Xiao-Wei Gong
    Wuhan Hospital of Traditional Chinese Medicine, Wuhan, China.
  • Si-Yu Bai
    Hubei University of Chinese Medicine, Wuhan, China.
  • En-Ze Lei
    Hubei University of Chinese Medicine, Wuhan, China.
  • Lian-Mei Lin
    Hubei University of Chinese Medicine, Wuhan, China.
  • Yao Chen
    Department of Galactophore Surgery, West China Hospital, Sichuan University, Chengdu, 610041, PR China.
  • Jian-Zhong Liu
    State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510275, China. lssljz@mail.sysu.edu.cn.