Study on Injury Prevention and Rehabilitation Models for Athletes Based on Data Mining

This article constructs a novel data mining-implemented algorithm for best injury prevention and rehabilitation policy among athletes. The algorithm considers performance measures, physiological measures, and history of injury in order to approximate the risk of injury and modify rehabilitation intervention accordingly. Incorporating real-time data analysis and concepts of machine learning in the model makes it more sensitive and accurate than the traditional approach. Individual contributions include specially tailored injury assessments and dynamic rehabilitation plans by necessity, filling loopholes in sports medicine procedure recommendations. This research contributes to the development of scalable, effective, and ethical solutions for fixing injuries in sport.