Development of a Predictive Modeling Framework for Athlete Injury Risk Assessment and Prevention: A Machine Learning Approach

Abstract

Athlete injuries are a pervasive issue in sports, resulting in significant consequences for athletic performance, career longevity, and overall well-being. To address this challenge, we developed a predictive modeling framework that leverages machine learning techniques to identify athletes at high risk of injury. Our approach integrates a range of athlete-specific data, including demographic, training, and performance metrics, to generate personalized injury risk profiles. A random forest classifier was employed to identify key predictors and classify athletes into high- or low-risk categories. Our results demonstrate a substantial improvement in injury prediction accuracy compared to traditional methods, highlighting the potential of machine learning in athlete injury prevention. This framework has important implications for coaches, trainers, and medical professionals, enabling targeted interventions and optimized athlete performance. Our study contributes to the growing body of research in sports analytics and machine learning, underscoring the importance of data-driven approaches in promoting athlete health and performance.