Medical decision support for football players based on machine learning historical injury data

• Jinhua Fang ^[1] ; Ting Xiang ^[2]
  1. [1] Guilin Institute of Information Technology,
  2. [2] Guilin University of Tourism
• Localización: Revista Internacional de Medicina y Ciencias de la Actividad Física y del Deporte, ISSN-e 1577-0354, Vol. 24, Nº. 96, 2024, págs. 479-489
• Idioma: inglés
• Enlaces
  • Texto completo
• Resumen

  • Utilizing massive clinical data in the field of football players injuries for assisted medical decision support is the core technology and inevitable development trend of smart healthcare. However, due to the characteristics of medical data such as feature redundancy and imbalance of data sample categories, it has been difficult for traditional data mining algorithms to be directly applied in medical data research. In this paper, we propose a data-driven football players injury prediction method based on the experimental study of football players injuries occurring during the learning process of youth professional soccer training, which is based on the machine learning method of decision tree classifier. Through a semester of data statistics and experiments, the model has a high accuracy of injury prediction, which can provide early warning of youth football players injuries and support medical decision-making.

• Referencias bibliográficas
  • Al-Asadi, M. A. M. (2018). Decision support system for a football team management by using machine learning techniques.
  • Anooj, P. (2012). Clinical decision support system: Risk level prediction of heart disease using weighted fuzzy rules. Journal of King ...
  • Claudino, J. G., Capanema, D. d. O., de Souza, T.V., Serrão, J. C., Machado Pereira, A. C., & Nassis, G. P. (2019). Current approaches...
  • Dash, S., Shakyawar, S. K., Sharma, M., & Kaushik, S. (2019). Big data in healthcare: management, analysis and future prospects. Journal...
  • Diehr, G., & Hunt, E. B. (1968). A comparison of memory allocation algorithms in a logical pattern recognizer: Department of Psychology,...
  • Du, J., Liu, Y., Yu, Y., & Yan, W. (2017). A prediction of precipitation data based on support vector machine and particle swarm optimization...
  • Gadaras, I., & Mikhailov, L. (2009). An interpretable fuzzy rule-based classification methodology for medical diagnosis. Artificial...
  • Gorzałczany, M. B., & Rudziński, F. (2017). Interpretable and accurate medical data classification–a multi-objective genetic-fuzzy optimization...
  • He, L., La, Y., Yan, Y., Wang, Y., Cao, X., Cai, Y., . . . Feng, Q. (2022). The prevalence and burden of four major chronic diseases in the...
  • Karnuta, J. M., Luu, B. C., Haeberle, H. S., Saluan, P. M., Frangiamore, S. J., Stearns, K. L., . . . Makhni, E. C. (2020). Machine learning...
  • Kim, J.-H. (2009). Estimating classification error rate: Repeated cross-validation, repeated hold-out and bootstrap. Computational...
  • Luu, B. C., Wright, A. L., Haeberle, H. S., Karnuta, J. M., Schickendantz, M. S., Makhni, E. C., . . . Ramkumar, P. N. (2020). Machine...
  • Ruddy, J. D., Cormack, S. J., Whiteley, R., Williams, M. D., Timmins, R. G., & Opar, D. A. (2019). Modeling the risk of team sport injuries:...
  • Seera, M., & Lim, C. P. (2014). A hybrid intelligent system for medical data classification. Expert systems with applications, 41(5),...
  • Soleimanian, F., Mohammadi, P., & Hakimi, P. (2012). Application of decision tree algorithm for data mining in healthcare operations:...
  • Theron, G. F. (2020). The use of data mining for predicting injuries in professional football players.
  • Wang, R., Kwong, S., Wang, X.-Z., & Jiang, Q. (2014). Segment based decision tree induction with continuous valued attributes. IEEE transactions...