Early Warning of Basketball Injury Risk Based on Attribute Reduction Algorithm

Abstract

Basketball is one of the students’ favorite ball games, and it is also one of the most popular sports for college students to carry out after class. Especially in recent years, with the spread of NBA culture around the world and the extensive development of CUBA in domestic colleges and universities, Yao Ming has appeared in China to compete in the NBA and achieve brilliant achievements. With the increasing investment in the number of basketball venues, basketball itself, as a sport with low dependence on venues, equipment, and people, has the characteristics of economy, convenience, and remarkable sports effect compared with other sports. College students’ basketball skills, basketball awareness, and love for basketball are increasing day by day. Attribute reduction algorithm is one of the core contents of knowledge discovery, which describes whether every attribute in the attribute set of information system is necessary and how to delete unnecessary knowledge. Based on the attribute reduction algorithm, this paper studies the early warning of basketball injury risk. The basketball injury can not only make athletes unable to participate in training or competition, but even cripple or lose their lives, which hinders the normal development of sports. Therefore, we should make a comprehensive and objective analysis of sports training to find out the causes of sports injuries, so as to prevent sports injuries. This algorithm takes the attribute frequency as the heuristic information and solves the attribute selection problem when the attribute frequency is the same.