Research on the analysis and prediction of college physical test results based on machine learning

Qing Liu

doi:10.1117/12.2675271

25 May 2023 Research on the analysis and prediction of college physical test results based on machine learning

Qing Liu

Proceedings Volume 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022); 1263612 (2023) https://doi.org/10.1117/12.2675271
Event: Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 2022, Shenyang, China

Abstract

The physical fitness test of college students mainly evaluates the physical condition and training effect through the test results of various items of students. It is a very sound and effective strategy to urge college students to actively participate in sports training. By analyzing the data of physical fitness test scores in colleges and universities over the years, the students with different physical fitness are classified and managed. According to the actual physical condition of students, different training plans are given to assist teachers in formulating more reasonable teaching programs for students. In order to reduce manual calculation and increase prediction efficiency, this paper proposes a comprehensive score prediction model based on machine learning. First, principal component analysis is used to transform multiple attributes with strong correlation into independent attributes without correlation, and the time and space for model training are reduced by eliminating redundancy. Secondly, the back propagation neural network algorithm is used to establish the physical fitness test prediction model, and the model is applied to the test data set to evaluate the performance of the model.

Citation Download Citation

Qing Liu "Research on the analysis and prediction of college physical test results based on machine learning", Proc. SPIE 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 1263612 (25 May 2023); https://doi.org/10.1117/12.2675271

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