Machine Learning to Predict Lower Extremity Musculoskeletal Injury Risk in Student Athletes.
injury risk
machine learning
random forest
sports science
student athlete
Journal
Frontiers in sports and active living
ISSN: 2624-9367
Titre abrégé: Front Sports Act Living
Pays: Switzerland
ID NLM: 101765780
Informations de publication
Date de publication:
2020
2020
Historique:
received:
26
06
2020
accepted:
27
10
2020
entrez:
21
12
2020
pubmed:
22
12
2020
medline:
22
12
2020
Statut:
epublish
Résumé
Injury rates in student athletes are high and often unpredictable. Injury risk factors are not agreed upon and often not validated. Here, we present a random-forest machine learning methodology for identifying the most significant injury risk factors and develop a model of lower extremity musculoskeletal injury risk in student athletes with physical performance metrics spanning joint strength measured with force transducers, postural stability measured using a force plate, and flexibility, measured with a goniometer, combined with previous injury metrics and athlete demographics. We tested our model in a population of 122 student athletes with performance metrics for the lower extremity musculoskeletal system and achieved an injury risk accuracy of 79% and identified significant injury risk factors, that could be used to increase accuracy of injury risk assessments, implement timely interventions, and decrease the number of career-ending or chronic injuries among student athletes.
Identifiants
pubmed: 33345141
doi: 10.3389/fspor.2020.576655
pmc: PMC7739722
doi:
Types de publication
Journal Article
Langues
eng
Pagination
576655Informations de copyright
Copyright © 2020 Henriquez, Sumner, Faherty, Sell and Bent.
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