Recent BJSM research shows that advanced machine learning models, notably Random Forest and XGBoost, enhance injury risk prediction in sports. By analyzing screening tests and performance data, the study addresses challenges like class imbalance, offering improved predictive accuracy. For instance, these models demonstrate high AUC scores, highlighting their potential in refining injury prevention strategies in athletic teams.
Q&A
- What are tree-based machine learning methods?
- How does class imbalance affect model performance?
- Why is data granularity important in these studies?
