Journal of the Korean Society of Industry Convergence (한국산업융합학회 논문집)

The Korean Society of Industry Convergence (한국산업융합학회)
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Comparative Evaluation of Machine Learning Models for Predicting Soccer Injury Types

  • Davronbek Malikov (Department of AI Convergence Engineering, Gyeongsang National University) ;
  • Jaeho Kim (Department of AI Convergence Engineering & Department of Software Engineering, Gyeongsang National University) ;
  • Jung Kyu Park (Department of Computer Engineering, Changshin University)
  • Received : 2024.01.23
  • Accepted : 2024.03.13
  • Published : 2024.04.30
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Abstract

Soccer is type of sport that carries a high risk of injury. Injury is not only cause in the unlucky soccer carrier and also team performance as well as financial effects can be worse since soccer is a team-based game. The duration of recovery from a soccer injury typically relies on its type and severity. Therefore, we conduct this research in order to predict the probability of players injury type using machine learning technologies in this paper. Furthermore, we compare different machine learning models to find the best fit model. This paper utilizes various supervised classification machine learning models, including Decision Tree, Random Forest, K-Nearest Neighbors (KNN), and Naive Bayes. Moreover, based on our finding the KNN and Decision models achieved the highest accuracy rates at 70%, surpassing other models. The Random Forest model followed closely with an accuracy score of 62%. Among the evaluated models, the Naive Bayes model demonstrated the lowest accuracy at 56%. We gathered information about 54 professional soccer players who are playing in the top five European leagues based on their career history. We gathered information about 54 professional soccer players who are playing in the top five European leagues based on their career history.

Keywords

Acknowledgement

This work was supported in part by the National Research Foundation of Korea (NRF) grant number (No. 2021R1F1A1063524 & 2021R1F1A1052129) and the research grant of the Gyeongsang National University in 2023.

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