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Machine Learning to Predict Lower Extremity Musculoskeletal Injury Risk in Student Athletes.
- Source :
-
Frontiers in sports and active living [Front Sports Act Living] 2020 Nov 19; Vol. 2, pp. 576655. Date of Electronic Publication: 2020 Nov 19 (Print Publication: 2020). - Publication Year :
- 2020
-
Abstract
- 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.<br /> (Copyright © 2020 Henriquez, Sumner, Faherty, Sell and Bent.)
Details
- Language :
- English
- ISSN :
- 2624-9367
- Volume :
- 2
- Database :
- MEDLINE
- Journal :
- Frontiers in sports and active living
- Publication Type :
- Academic Journal
- Accession number :
- 33345141
- Full Text :
- https://doi.org/10.3389/fspor.2020.576655