Injury Prediction in Korean Adult Field Hockey Players Using Machine Learning and SHAP-Based Feature Importance Analysis

Field hockey involves repetitive high-intensity movements and physical contact, posing a high risk of injury. However, studies developing injury prediction models without relying on expensive tools such as GPS remain limited. This study aimed to develop an explainable AI model that predicts injury occurrence using only simple questionnaire-based data and visually identifies key predictors. Survey data were collected from 239 adult players registered with the Korea Field Hockey Association in 2024, including university and professional team athletes. Ten variables were used: sex, team affiliation, playing experience, player level, warm-up duration, weekly training hours and days, and physical indicators (age, height, weight). Injury was defined as an event within the past year that resulted in being unable to train for more than 24 h. Logistic Regression, Random Forest, and XGBoost models were compared. The final model—Logistic Regression—underwent SHAP-based visualization for interpretability. The Logistic Regression model showed the best performance in recall (0.6810 ± 0.0983), F1-score (0.6260 ± 0.0499), and AUC (0.6515 ± 0.0393). SHAP analysis identified Group, Training Time, Weight, and Player Level as key predictors, and visualized their contributions to individual predictions. This study demonstrates that a lightweight, interpretable injury prediction model using only simple survey data can achieve practical performance. This approach offers valuable insights for real-world applications and the development of injury prevention strategies.