DECISION SUPPORT FOR BASKETBALL ATHLETE INJURY PROTECTION BASED ON PATTERN RECOGNITION

Abstract

Basketball is an intense competitive team sport, and sports injuries are a problem that athletes and coaches must face. Modern basketball sports body collision and confrontation is very powerful, so the risk of athletes in the training and competition process the chance of injury increased. To be able to find out the potential risk of injury early, to avoid serious injury law injury, this paper uses machine learning and pattern recognition technology, based on decision tree and random forest algorithm combined with wearable equipment to build basketball sports injury decision support model, to provide technical support for basketball player injury judgment. Through experimental verification, the decision support model proposed in this paper can effectively determine the injury situation of athletes and provide support for coaches to make further decisions.