Research on a Mobile Prediction Platform for Dynamic Changes in Athletic Performance Based on Screening Factors.

Most of the traditional athletic performance dynamic change prediction platforms use gray correlation and neural networks to establish prediction methods, and the prediction results are inaccurate and time-consuming. Therefore, the construction of a mobile prediction platform for dynamic changes in athletic performance based on screening factors is proposed to improve the accuracy of its prediction results and shorten the prediction time. On the basis of hardware, the main body of students is determined, the athletic performance data of students is collected by the physical education management system of colleges and universities, and the missing data is filled by the criterion function of K-average algorithm. The KNN algorithm is used to make decisions to obtain the most representative predictors of athletic performance data, and the characteristics of students' athletic performance are extracted. The effective factors are selected according to the tenfold cross-validation fold MSE minimum principle of SVM, and the mandatory screening based on SVM is used to assign weights to each reserved factor. According to the weighted result of the obtained factors, the influencing factors of athletic performance are analyzed, and the weighting process is completed for the factors, and the athletic performance prediction is realized by using support vector machine. In the platform established in this paper, the students' 1000-m running results are predicted. The simulation results show that, compared with the traditional platform, the constructed platform has higher prediction accuracy and takes less time. [ABSTRACT FROM AUTHOR]