9卷 第2期
School of Statistics and Mathematics, Yunnan University of Finance and Economics, Kunming, Yunnan, 650221, China
纸质出版日期：2025-04-28
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(2025). Research on athlete injury prediction model based on Logistic. 创业与创新（英文版）, 9(2), 31-36.
Wang Fangyong.Research on athlete injury prediction model based on Logistic[J].Entrepreneurship and Innovation,2025,09(02):31-36.
(2025). Research on athlete injury prediction model based on Logistic. 创业与创新（英文版）, 9(2), 31-36. DOI： 10.47297/wspeiWSP2516-253506.20250902.
Wang Fangyong.Research on athlete injury prediction model based on Logistic[J].Entrepreneurship and Innovation,2025,09(02):31-36. DOI： 10.47297/wspeiWSP2516-253506.20250902.
In today's sports field
the prevention and management of athletes' injuries have become crucial. Timely and accurate prediction of athletes' injury risk can not only help the team to make a reasonable training plan
but also greatly reduce the loss caused by injury. In this study
based on injury_data datasets
a model of athlete injury prediction was constructed by using Logistic regression model
aiming to achieve the binary classification prediction of athlete injury risk. The injury_data datasets contains several key pieces of information about athletes
such as type of exercise
intensity of training
prior injury history
etc.
which are critical to predicting whether an athlete is at risk of injury. We ensure that the data used for modeling is of high quality and accuracy through elaborate pre-processing steps
including missing value processing and outlier detection.In the process of constructing the Logistic regression model
Maximum Likelihood Estimation (MLE) is used to determine the optimal model parameters. In order to evaluate the performance of the model
we use the likelihood ratio test as the evaluation criterion of model fit degree. The experimental results show that our model can effectively predict the injury risk of athletes
and its prediction accuracy meets the preliminary application requirements.Through this study
we have confirmed the feasibility and effectiveness of the application of Logistic regression model in the field of athlete injury prediction
and hope that this method can provide technical reference for subsequent research in similar fields. Future work will focus on introducing more data features and exploring more advanced prediction models to further improve the accuracy of predictions and help optimize athlete health management and injury prevention strategies.
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