Volume 9, Issue 2
School of Statistics and Mathematics, Yunnan University of Finance and Economics, Kunming, Yunnan, 650221, China
Published：28 April 2025
Scan QR Code
Wang Fangyong.Research on athlete injury prediction model based on Logistic[J].Entrepreneurship and Innovation,2025,09(02):31-36.
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.
Leckey C , van Dyk N , Doherty C , et al . Machine learning approaches to injury risk prediction in sport: a scoping review with evidence synthesis [J ] . British Journal of Sports Medicine , 2024 .
Martins F , Przednowek K , França C , et al . Predictive modeling of injury risk based on body composition and selected physical fitness tests for elite football players [J ] . Journal of Clinical Medicine , 2022 , 11 ( 16 ): 4923 .
Luu B C , Wright A L , Haeberle H S , et al . Machine learning outperforms logistic regression analysis to predict next-season NHL player injury: an analysis of 2322 players from 2007 to 2017 [J ] . Orthopaedic journal of sports medicine , 2020 , 8 ( 9 ): 2325967120953404 .
Musat C L , Mereuta C , Nechita A , et al . Diagnostic Applications of AI in Sports: A Comprehensive Review of Injury Risk Prediction Methods [J ] . Diagnostics , 2024 , 14 ( 22 ): 2516 .
Shaw A , Newman P , Witchalls J , et al . Externally validated machine learning algorithm accurately predicts medial tibial stress syndrome in military trainees: a multicohort study [J ] . BMJ Open Sport & Exercise Medicine , 2023 , 9 ( 2 ).
Goggins L , Warren A , Osguthorpe D , et al . Detecting injury risk factors with algorithmic models in elite women's pathway cricket [J ] . International journal of sports medicine , 2022 , 43 ( 04 ): 344 - 349 .
Thornton H R , Delaney J A , Duthie G M , et al . Importance of various training-load measures in injury incidence of professional rugby league athletes [J ] . International journal of sports physiology and performance , 2017 , 12 ( 6 ): 819 - 824 .
Henriquez M , Sumner J , Faherty M , et al . Machine learning to predict lower extremity musculoskeletal injury risk in student athletes [J ] . Frontiers in sports and active living , 2020 , 2 : 576655 .
Briand J , Deguire S , Gaudet S , et al . Monitoring variables influence on random forest models to forecast injuries in short-track speed skating [J ] . Frontiers in sports and active living , 2022 , 4 : 896828 .
Farhadian M , Torkaman S , Mojarad F . Random forest algorithm to identify factors associated with sports-related dental injuries in 6 to 13-year-old athlete children in Hamadan, Iran-2018-a cross-sectional study [J ] . BMC sports science, medicine and rehabilitation , 2020 , 12 : 1 - 9 .
Rommers N , Rössler R , Verhagen E , et al . A machine learning approach to assess injury risk in elite youth football players [J ] . Medicine and science in sports and exercise , 2020 , 52 ( 8 ): 1745 - 1751 .
Huang Y , Huang S , Wang Y , et al . A novel lower extremity non-contact injury risk prediction model based on multimodal fusion and interpretable machine learning [J ] . Frontiers in physiology , 2022 , 13 : 937546 .
Lövdal S S , Den Hartigh R J R , Azzopardi G . Injury prediction in competitive runners with machine learning [J ] . International journal of sports physiology and performance , 2021 , 16 ( 10 ): 1522 - 1531 .
Lu Y , Pareek A , Lavoie-Gagne O Z , et al . Machine learning for predicting lower extremity muscle strain in national basketball association athletes [J ] . Orthopaedic Journal of Sports Medicine , 2022 , 10 ( 7 ): 23259671221111742 .
Karnuta J M , Luu B C , Haeberle H S , et al . Machine learning outperforms regression analysis to predict next-season Major League Baseball player injuries: epidemiology and validation of 13,982 player-years from performance and injury profile trends, 2000-2017 [J ] . Orthopaedic journal of sports medicine , 2020 , 8 ( 11 ): 2325967120963046 .
Jauhiainen S , Kauppi J P , Leppänen M , et al . New machine learning approach for detection of injury risk factors in young team sport athletes [J ] . International journal of sports medicine , 2021 , 42 ( 02 ): 175 - 182 .
Related Articles
Related Author
Related Institution
京公网安备11010802024621