期刊
INTERNATIONAL JOURNAL OF SPORTS MEDICINE
卷 40, 期 5, 页码 344-353出版社
GEORG THIEME VERLAG KG
DOI: 10.1055/a-0826-1955
关键词
injury prevention; injury risk; modeling; screening; decision-making
资金
- Ministerio de Economia y Competitividad from Spain [FPI BES-2015-07200]
- Seneca Foundation from Spain [20366/PD/17]
Hamstring 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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