Machine Learning to Predict Lower Extremity Musculoskeletal Injury Risk in Student Athletes.

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 ris

SUBMITTER: Henriquez M 

PROVIDER: S-EPMC7739722 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature