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Explainable Machine Learning Techniques to Predict Muscle Injuries in Professional Soccer Players through

Mailyn Calderón-Díaz1,2,3, Rony Silvestre Aguirre4, Juan P Vásconez1

  • 1Faculty of Engineering, Universidad Andres Bello, Santiago 7550196, Chile.

Sensors (Basel, Switzerland)
|January 11, 2024
PubMed
Summary

Hamstring strain injuries in soccer players can be predicted using biomechanical data. Maximum hamstring strength and stiffness are key biomarkers for preventing these common sports injuries.

Keywords:
XGBoosthamstring injuriesmachine learning explainabilitysoccer playersport medicine

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Area of Science:

  • Sports Medicine
  • Biomechanical Analysis
  • Artificial Intelligence in Sports

Background:

  • Hamstring strain injuries (HSIs) are the most common injury in professional soccer, leading to significant missed playing time.
  • Identifying key risk factors and effective prevention strategies for HSIs is challenging due to multifactorial causes.
  • Current AI applications in sports injury often prioritize model performance over interpretability, hindering clinical adoption.

Purpose of the Study:

  • To identify biomarkers for muscle injuries in professional soccer players using biomechanical analysis.
  • To develop and evaluate machine learning models for accurate injury detection and prediction.
  • To enhance the interpretability of AI models for medical teams and trainers.

Main Methods:

  • Employed a range of machine learning (ML) algorithms including Decision Trees, SVM, KNN, ANNs, and XGBoost.
  • Utilized biomechanical analysis to identify potential injury biomarkers.
  • Applied XGBoost to determine the most significant predictive features for hamstring injuries.

Main Results:

  • Maximum hamstring muscle strength and hamstring muscle stiffness were identified as the most effective differentiating variables and reliable predictors.
  • XGBoost achieved a precision of up to 78% in injury prediction among the 35 techniques evaluated.
  • The findings align with existing literature, suggesting the potential for AI-driven injury prevention.

Conclusions:

  • Biomechanical factors like muscle strength and stiffness are crucial for predicting hamstring injuries in soccer.
  • Interpretable AI models, such as XGBoost, can improve the reliability of injury prediction for medical professionals.
  • Further research is needed to refine AI models and confirm findings for specific sports contexts.