Ankle Injury Prevention In Soccer Using Machine Learning: Bibliometric Analysis

Bibliometric analysis seeks to evaluate through statistical methods the scientific activity on the lines and trends of research, the evolution of studies, the relationships between publications, journals and collaboration between researchers. The use of these studies can guide researchers on the evo...

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Detalhes bibliográficos
Autores: Burbano Fernández, Marlon Felipe, Villaquirán Hurtado, Andrés Felipe, Celis Quisobony, Viviana Marcela, Hoyos Quisoboni, Jeffry Alexander, Molano Tobar, Nancy Janneth
Formato: artículo
Fecha de publicación:2024
País:España
Recursos:Universidad Católica San Antonio de Murcia (UCAM)
Repositorio:RIUCAM. Repositorio Institucional de la Universidad Católica San Antonio de Murcia
OAI Identifier:oai:repositorio.ucam.edu:10952/8268
Acesso em linha:http://hdl.handle.net/10952/8268
Access Level:acceso abierto
Palavra-chave:Injury sports
Lesiones deportivas
Bibliometría
Inteligencia artificial
Ankle
Artificial intelligence
Bibliometrics
Soccer
Fútbol
Tobillo
Descrição
Resumo:Bibliometric analysis seeks to evaluate through statistical methods the scientific activity on the lines and trends of research, the evolution of studies, the relationships between publications, journals and collaboration between researchers. The use of these studies can guide researchers on the evolution of research processes related to injury prevention in soccer, using machine learning. The aim of this study is to analyze the scientific activity related to machine learning in the prevention of ankle injuries in soccer. The present study presents three moments: Data capture, Analysis of the information based on software (Scimat, VosViewer, Use of Text mining with R), discussion and conclusions. As for the results, the evolution of the words and networks generated shows an increase in studies relating the words “sport”, “ankle”, “risk factors” and “technology” (mobile applications, computational methods, wireless communication). An evolution of research in terms of the use of machine learning in injury prevention, visualization of knowledge networks and support among researchers in recent years is evident, as well as the growth of publications and the increase of networks and interaction between words.