Predictive Analytics for Injury Prevention in Physical Education Using Machine Learning Models

In recent years, there has been a growing concern over the increasing number of injuries among high school students participating in physical education programs, particularly during sports activities. These injuries, ranging from minor sprains to stress fractures, can significantly impact student health and disrupt the learning process. Focused on addressing this issue, applying predictive analytics along with machine learning models to figure out the possible at-risk group of students timely interventions are set in motion by this study regarding the possibility to predict and potentially prevent injuries based on machine learning algorithms. Comprehensive datasets containing a combination of all demographic information related to physical and other metrics; injury history information; and detailed activity data reported from wearable equipment will be provided. Models that were applied to achieve this end are Recurrent Neural Networks (RNN), Random Forest (RF), Support Vector Machines (SVM), and Artificial Neural Networks (ANN). Results obtained from this indicate that the RNN model is the strongest one compared to others regarding accuracy since it identified the widest range of injury types with a very minor error. Random Forest also did quite well, whereas SVM and ANN showed lower accuracies, mostly while predicting complex injuries. Hence, the general results indicate that machine learning via real-time data analysis from wearables can significantly enhance injury prevention practices in PE. This approach provides educators and coaches with early warnings regarding the potential injury risks, allowing them to undertake preventive measures in improving both safety among students and athletic performance.