Publication: Machine learning application in predicting anterior cruciate ligament injury among basketball players
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Date
2025-01
Authors
Longfei, Guo
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Abstract
Anterior cruciate ligament (ACL) injury is among the most prevalent injuries in
athletes, significantly impacting their competitive performance. Preventing ACL
injury is challenging due to their multifactorial nature. Machine learning-based data
mining techniques have shown significant potential in identifying risk factors
associated with ACL injury. This study aimed to assess the predictive capability of
these features using machine learning models. Data on athlete’s profile, physical
function, specialized qualities, three-dimensional movement analysis, and
simultaneous electromyography were prospectively collected from 104 male
basketball players. A one-year follow-up was conducted to monitor ACL injury,
identifying n=11 injured players. Four machine learning algorithms—Random Forest
(RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost), and
Logistic Regression (LR)—were developed to predict ACL injury. The optimal
model was selected based on the mean area under the receiver operating
characteristic curve (AUC-ROC) across 10 cross-validation runs and was used with
Shapley Additive exPlanations to analyze the risk factors. The results show that
AUC-ROC values varied slightly across repetitions and methods (0.66-0.80), the best
classifier was RF. SHAP analysis identified key feature with the highest predictive
value for ACL injury during specific sports motions. Emergency Stop phase:
Increased knee flexion moment, posterior ground reaction forces, knee flexion angle,
and overactivation of the lateral quadriceps and rectus femoris. Initial Acceleration
phase: Elevated knee internal rotation torque and lateral stress on the lower limbs.
Side-Cutting phase: Reduced tibial inclination and hip flexion angles, increased
ankle inversion angle, ankle eversion moment, and excessive lateral thigh muscle
activation. Furthermore, poor stability in the non-dominant leg, weak Squat Jump
performance, training loads exceeding 15 hours per week, and prior injury history
were significant ACL injury predictors. This study emphasizes the Machine Learning
model's effectiveness in predicting ACL injury, highlighting biomechanical metrics,
functional attributes, and historical feature as critical predictors for targeted
prevention strategies.