A field-based approach to determine soft tissue injury risk in elite futsal using novel machine learning techniques

Ruiz-Pérez, Iñaki, López-Valenciano, Alejandro, Hernández-Sánchez, Sergio, Puerta-Callejón, José M, De Ste Croix, Mark B ORCID: 0000-0001-9911-4355, Sainz de Baranda, Pilar and Ayala, Francisco ORCID: 0000-0003-2210-7389 (2021) A field-based approach to determine soft tissue injury risk in elite futsal using novel machine learning techniques. Frontiers in Psychology, 12. Art 610210. doi:10.3389/fpsyg.2021.610210

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Abstract

Lower extremity non-contact soft tissue (LE-ST) injuries are prevalent in elite futsal. The purpose of this study was to develop robust screening models based on pre-season measures obtained from questionnaires and field-based tests to prospectively predict LE-ST injuries after having applied a range of supervised Machine Learning techniques. One hundred and thirty-nine elite futsal players underwent a pre-season screening evaluation that included individual characteristics; measures related to sleep quality, athlete burnout, psychological characteristics related to sport performance and self-reported perception of chronic ankle instability. A number of neuromuscular performance measures obtained through three field-based tests (isometric hip strength, dynamic postural control [Y-Balance] and lower extremity joints range of motion [ROM-Sport battery]) were also recorded. Injury incidence was monitored over one competitive season. There were 25 LE-ST injuries. Only those groups of measures from two of the field-based tests (ROM-Sport battery and Y-Balance), as independent data sets, were able to build robust models (area under the receiver operating characteristic curve [AUC] score ≥ 0.7) to identify elite futsal players at risk of sustaining a LE-ST injury. Unlike the measures obtained from the five questionnaires selected, the neuromuscular performance measures did build robust prediction models (AUC score ≥ 0.7). The inclusion in the same data set of the measures recorded from all the questionnaires and field-based tests did not result in models with significantly higher performance scores. The model generated by the UnderBagging technique with a cost-sensitive SMO as the base classifier and using only four ROM measures reported the best prediction performance scores (AUC = 0.767, true positive rate = 65.9% and true negative rate = 62%). The models developed might help coaches, physical trainers and medical practitioners in the decision-making process for injury prevention in futsal.

Item Type:	Article
Article Type:	Article
Uncontrolled Keywords:	Injury prevention; Modelling; Screening; Decision-making; Algorithm; Decision tree
Related URLs:	Dataset
Subjects:	G Geography. Anthropology. Recreation > GV Recreation Leisure > GV557 Sports > GV861 Ball games: Baseball, football, golf, etc. Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > RC Internal medicine > RC1200 Sports Medicine
Divisions:	Schools and Research Institutes > School of Education and Science
Research Priority Areas:	Health, Life Sciences, Sport and Wellbeing
Depositing User:	Rhiannon Goodland
Date Deposited:	08 Jan 2021 16:59
Last Modified:	31 Aug 2023 09:07
URI:	https://eprints.glos.ac.uk/id/eprint/9259

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