Attribute Reduction Algorithm Based Early Warning Model of Sports Injury

Abstract

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%.Povzetek: Razvit je sistem za zgodnje napovedovanje športnih poškodb.

A Radial Basis Network-based Early Warning Algorithm for Physical Injuries in Marathon Athletes

“…The results of the study showed that there was a significant correlation between the adequacy of athletes' preparation activities and the rationality of training programs and athletes' injuries. Wang and his research partner developed a mutual information sports injury warning model based on an attribute parsimony algorithm, which was designed for youth athletics [7]. Simulation experiments found that the model was able to warn youth track and field athletes of injuries with 80% correctness, but the model suffered from a local optimal solution.…”

“…When nN  , the control parameters must be such that the inertia weights can reach or converge to (min)  , and the inertia weights at this time are shown in Eq. (7).…”

Injury Risk Prediction and Prevention Algorithm for Athletes Based on Data Mining

“…These datasets undergo preprocessing to clean and transform the data into a format suitable for analysis [10].Next, data mining techniques such as machine learning algorithms are applied to uncover hidden patterns and relationships within the data. Classification algorithms can be utilized to categorize athletes into different risk groups based on their likelihood of injury [11]. Clustering techniques can identify subgroups of athletes with similar risk profiles, allowing for targeted prevention strategies.…”