Sport Injury Prediction using Machine Learning

In the dynamic world of sports, injury prevention is crucial for maintaining athlete performance and longevity. This research explores advanced machine learning techniques to predict sports injuries with unprecedented accuracy. By analyzing a comprehensive dataset of 200 athlete records containing 17 key attributes, the study demonstrates the potential of predictive analytics in sports science. The research systematically evaluated multiple machine learning models, including Support Vector Machine (SVM), Decision Tree, Random Forest, XGBoost, and an innovative ensemble approach. Through rigorous preprocessing techniques such as feature engineering, normalization, and categorical encoding, the models were trained to identify complex injury risk patterns. The results are compelling: while traditional models like SVM achieved 92% accuracy, the ensemble learning approach reached an exceptional 97.5% accuracy in predicting athlete injuries. This breakthrough highlights the power of combining multiple algorithmic approaches to enhance predictive performance. By leveraging features including training intensity, recovery patterns, performance metrics, and physiological characteristics, the model offers a data-driven strategy for injury prevention.Beyond its technical achievements, this study provides a framework for transforming sports injury management. It demonstrates how machine learning can offer coaches and medical professionals actionable insights, enabling proactive interventions that could potentially reduce injury risks and extend athletic careers. The research not only advances sports analytics but also sets a new standard for applying sophisticated machine learning techniques to athlete health and performance optimization.