Attribute Reduction Algorithm Based Early Warning Model of Sports Injury
With the continuous development of track and field sports, a relatively complete and scientific system has been formed in terms of training, and more attention has been paid to the planned training of teenagers for many years, psychological training and recovery training, and strengthening medical supervision and scientific research. In order to speed up the scientific training of track and field and improve the level of track and field training in China, in view of the characteristics of track and field injuries such as multiple causes and complexity, this paper analyzes and studies the attribute reduction algorithm based on attribute reduction algorithm, and proposes a sports injury early warning model based on mutual information. Taking the reduction results as input neurons, a BP neural network with hidden layer is established in MATLAB environment. The results show that: call 85% of the track and field athletes in the sample information as training samples for training. After the simulation experiment of the model, it is found that the error value of the prediction results is basically controlled in the range of-0.025-0.05, which meets the requirements of early warning accuracy of injury risk level of athletes, and the accuracy rate of early warning of sports injury risk reaches 100%.
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