A Preventive Model for Hamstring Injuries in Professional Soccer: Learning Algorithms

International Journal of Sports Medicine - Tập 40 Số 05 - Trang 344-353 - 2019
Francisco Ayala1, Alejandro López‐Valenciano1, José A. Gámez2, Mark De Ste Croix3, Francisco J. Vera-García1, María Pilar García-Vaquero1, Iñaki Ruiz‐Pérez1, Gregory D. Myer4,5,6
1Department of Sport Science, Sport Research Centre, Miguel Hernández University of Elche, Elche (Alicante), Spain.
2Escuela Superior de Ingeniería Informática, Universidad de Castilla-La Mancha, Albacete, Spain
3School of Sport and Exercise, University of Gloucestershire, Gloucester, United Kingdom of Great Britain and Northern Ireland
4Department of Pediatrics and Orthopaedic Surgery, College of Medicine, University of Cincinnati, Cincinnati, Ohio, United States
5The Micheli Center for Sports Injury Prevention, Waltham, MA, United States
6The SPORT Center, Division of Sports Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, United States

Tóm tắt

AbstractHamstring strain injury (HSI) is one of the most prevalent and severe injury in professional soccer. The purpose was to analyze and compare the predictive ability of a range of machine learning techniques to select the best performing injury risk factor model to identify professional soccer players at high risk of HSIs. A total of 96 male professional soccer players underwent a pre-season screening evaluation that included a large number of individual, psychological and neuromuscular measurements. Injury surveillance was prospectively employed to capture all the HSI occurring in the 2013/2014 season. There were 18 HSIs. Injury distribution was 55.6% dominant leg and 44.4% non-dominant leg. The model generated by the SmooteBoostM1 technique with a cost-sensitive ADTree as the base classifier reported the best evaluation criteria (area under the receiver operating characteristic curve score=0.837, true positive rate=77.8%, true negative rate=83.8%) and hence was considered the best for predicting HSI. The prediction model showed moderate to high accuracy for identifying professional soccer players at risk of HSI during pre-season screenings. Therefore, the model developed might help coaches, physical trainers and medical practitioners in the decision-making process for injury prevention.

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International Journal of Sports Medicine

Tập 40 Số 05

344-353

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