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A Machine Learning Model for Post-Concussion Musculoskeletal Injury Risk in Collegiate Athletes
Claudio C Claros1, Melissa N Anderson2, Wei Qian3
1Department of Electrical and Computer Engineering, University of Delaware, Newark, DE, USA.
Sports Medicine (Auckland, N.Z.)
|March 27, 2025
Summary
Machine learning models can predict post-concussion musculoskeletal injuries in collegiate athletes. This approach identifies high-risk athletes for targeted injury prevention strategies.
Area of Science:
- Sports Medicine
- Biostatistics
- Machine Learning in Healthcare
Background:
- Emerging evidence suggests collegiate athletes have an increased risk of post-concussion musculoskeletal injuries.
- Identifying athletes most susceptible to these injuries requires further investigation.
Purpose of the Study:
- To develop a machine learning model for predicting post-concussion musculoskeletal injury risk in collegiate athletes.
- To integrate a comprehensive set of variables into the predictive model.
Main Methods:
- A risk model was developed using a dataset of 194 collegiate athletes.
- 135 variables including health, athletic history, concussion criteria, and assessment outcomes were analyzed.
- Machine learning techniques included weight of evidence transformation, L1/L2-regularized logistic regression, and Akaike Information Criterion for model selection.
Main Results:
- The final model, with 48 predictive variables, demonstrated significant predictive performance (Area Under the Curve = 0.82).
- Key predictors included baseline and acute cognitive, balance, and reaction assessments.
- The model achieved 79% sensitivity and 95% precision at a 6.67% false-positive rate.
Conclusions:
- The study supports the development of a sensitive and specific injury risk model for post-concussion musculoskeletal injuries.
- This machine learning approach, using comprehensive data, can help clinicians identify and target high-risk student athletes for injury prevention.
