AUTHOR=Henriquez Maria , Sumner Jacob , Faherty Mallory , Sell Timothy , Bent Brinnae TITLE=Machine Learning to Predict Lower Extremity Musculoskeletal Injury Risk in Student Athletes JOURNAL=Frontiers in Sports and Active Living VOLUME=2 YEAR=2020 URL=https://www.frontiersin.org/journals/sports-and-active-living/articles/10.3389/fspor.2020.576655 DOI=10.3389/fspor.2020.576655 ISSN=2624-9367 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.