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

Autor:	Gong XW; Wuhan Hospital of Traditional Chinese Medicine, Wuhan, China.; Hubei University of Chinese Medicine, Wuhan, China., Bai SY; Hubei University of Chinese Medicine, Wuhan, China., Lei EZ; Hubei University of Chinese Medicine, Wuhan, China., Lin LM; Hubei University of Chinese Medicine, Wuhan, China., Chen Y; Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, China.; Affiliated Hospital of Hubei University of Chinese Medicine, Wuhan, China.; Hubei Academy of Traditional Chinese Medicine, Wuhan, China., Liu JZ; Hubei University of Chinese Medicine, Wuhan, China.; Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, China.; Affiliated Hospital of Hubei University of Chinese Medicine, Wuhan, China.; Hubei Academy of Traditional Chinese Medicine, Wuhan, China.
Jazyk:	angličtina
Zdroj:	Medicine [Medicine (Baltimore)] 2024 Sep 06; Vol. 103 (36), pp. e39610.
DOI:	10.1097/MD.0000000000039610
Abstrakt:	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. Methods: The Web of Science core collection was the primary database source for this study, which collected publications on obesity research using machine learning methods over the last 20 years from January 1, 2004, to December 31, 2023. Only articles and reviews that fit the criteria were selected for bibliometric analysis, and in terms of language, only English was accepted. VOSviewer, CiteSpace, and Excel were the primary software utilized. Results: Between 2004 and 2023, the number of publications on obesity research using machine learning methods increased exponentially. Eventually, 3286 publications that met the eligibility criteria were searched. According to the collaborative network analysis, the United States has the greatest volume of publications, indicating a significant influence on this research. coauthor's analysis showed the authoritative one in this field is Leo Breiman. Scientific Reports is the most widely published journal. The most referenced publication is "R: a language and environment for statistical computing." An analysis of keywords shows that deep learning, support vector machines, predictive models, gut microbiota, energy expenditure, and genome are hot topics in this field. Future research directions may include the relationship between obesity and its consequences, such as diabetic retinopathy, as well as the interaction between obesity and epidemiology, such as COVID-19. Conclusion: Utilizing bibliometrics as a research tool and methodology, this study, for the first time, reveals the intrinsic relationship and developmental pattern among obesity research using machine learning methods, which provides academic references for clinicians and researchers in understanding the hotspots and cutting-edge issues as well as the developmental trend in this field to detect patients' obesity problems early and develop personalized treatment plans. Competing Interests: The authors have no conflicts of interest to disclose. (Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.)
Databáze:	MEDLINE
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