A Preventive Model for Muscle Injuries: A Novel Approach based on Learning Algorithms

ABSTRACTIntroductionThe application of contemporary statistical approaches coming from Machine Learning and Data Mining environments to build more robust predictive models to identify athletes at high risk of injury might support injury prevention strategies of the future.PurposeThe purpose was to analyse and compare the behaviour of numerous machine learning methods in order to select the best performing injury risk factor model to identify athlete at risk of lower extremity muscle injuries (MUSINJ).MethodsA total of 132 male professional soccer and handball players underwent a pre-season screening evaluation which included personal, psychological and neuromuscular measures. Furthermore, injury surveillance was employed to capture all the MUSINJ occurring in the 2013/2014 seasons. The predictive ability of several models built by applying a range of learning techniques were analysed and compared.ResultsThere were 32 MUSINJ over the follow up period, 21 (65.6%) of which corresponded to the hamstrings, three to the quadriceps (9.3%), four to the adductors (12.5%) and four to the triceps surae (12.5%). A total of 13 injures occurred during training and 19 during competition. Three players were injured twice during the observation period so the first injury was used leaving 29 MUSINJ that were used to develop the predictive models. The model generated by the SmooteBoost technique with a cost-sensitive ADTree as the base classifier reported the best evaluation criteria (area under the receiver operating characteristic curve score = 0.747, true positive rate = 65.9%, true negative rate = 79.1) and hence was considered the best for predicting MUSINJ.ConclusionsThe prediction model showed moderate accuracy for identifying professional soccer and handball players at risk of MUSINJ. Therefore, the model developed might help in the decision-making process for injury prevention. Introduction The application of contemporary statistical approaches coming from Machine Learning and Data Mining environments to build more robust predictive models to identify athletes at high risk of injury might support injury prevention strategies of the future. Purpose The purpose was to analyse and compare the behaviour of numerous machine learning methods in order to select the best performing injury risk factor model to identify athlete at risk of lower extremity muscle injuries (MUSINJ). Methods A total of 132 male professional soccer and handball players underwent a pre-season screening evaluation which included personal, psychological and neuromuscular measures. Furthermore, injury surveillance was employed to capture all the MUSINJ occurring in the 2013/2014 seasons. The predictive ability of several models built by applying a range of learning techniques were analysed and compared. Results There were 32 MUSINJ over the follow up period, 21 (65.6%) of which corresponded to the hamstrings, three to the quadriceps (9.3%), four to the adductors (12.5%) and four to the triceps surae (12.5%). A total of 13 injures occurred during training and 19 during competition. Three players were injured twice during the observation period so the first injury was used leaving 29 MUSINJ that were used to develop the predictive models. The model generated by the SmooteBoost technique with a cost-sensitive ADTree as the base classifier reported the best evaluation criteria (area under the receiver operating characteristic curve score = 0.747, true positive rate = 65.9%, true negative rate = 79.1) and hence was considered the best for predicting MUSINJ. Conclusions The prediction model showed moderate accuracy for identifying professional soccer and handball players at risk of MUSINJ. Therefore, the model developed might help in the decision-making process for injury prevention. Corresponding Author. Francisco Ayala. Sports Research Centre, Miguel Hernandez University of Elche. Avda. de la Universidad s/n. 03202 Elche, Alicante, Spain. Email address: fayala@umh.es, Fax: +0034965222409. Alejandro López-Valenciano were supported by predoctoral grant given by Ministerio de Educación, Cultura y Deporte (FPU) from Spain. We certify that no party having a direct interest in the results of the research supporting this article has or will confer a benefit on us or on any organization which we are associated, do not constitute endorsement by ACSM and they are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation. Accepted for Publication: 14 December 2017 © 2017 American College of Sports Medicine

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