Machine Learning Outperforms Logistic Regression Analysis to Predict Next-Season NHL Player Injury: An Analysis of 2322 Players From 2007 to 2017

BACKGROUND: The opportunity to quantitatively predict next-season injury risk in the National Hockey League (NHL) has become a reality with the advent of advanced computational processors and machine learning (ML) architecture. Unlike static regression analyses that provide a momentary prediction, M...

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Detalles Bibliográficos
Autores principales: Luu, Bryan C., Wright, Audrey L., Haeberle, Heather S., Karnuta, Jaret M., Schickendantz, Mark S., Makhni, Eric C., Nwachukwu, Benedict U., Williams, Riley J., Ramkumar, Prem N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7522848/
https://www.ncbi.nlm.nih.gov/pubmed/33029545
http://dx.doi.org/10.1177/2325967120953404

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7522848/
https://www.ncbi.nlm.nih.gov/pubmed/33029545
http://dx.doi.org/10.1177/2325967120953404

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