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Prediction of Injuries in CrossFit Training: A Machine Learning Perspective

AIDEAS OÜ, Narva mnt 5, 10117 Tallinn, Harju Maakond, Estonia
Department of Computer Science and Biomedical Informatics, School of Science, University of Thessaly, 35131 Lamia, Greece
School of Health Sciences, University of Thessaly, Department of Physiotherapy, 35100 Lamia, Greece
“Physio’clock” Advanced Physiotherapy Center, 41223 Larissa, Greece
Department of Energy Systems, University of Thessaly, Geopolis Campus, 41500 Larisa, Greece
Institute for Bio-Economy & Agri-Technology, Center for Research and Technology Hellas, 60361 Volos, Greece
Author to whom correspondence should be addressed.
Academic Editors: Panagiotis Pintelas and Ioannis E. Livieris
Algorithms 2022, 15(3), 77;
Received: 30 January 2022 / Revised: 18 February 2022 / Accepted: 21 February 2022 / Published: 24 February 2022
(This article belongs to the Special Issue Ensemble Algorithms and/or Explainability)
CrossFit has gained recognition and interest among physically active populations being one of the most popular and rapidly growing exercise regimens worldwide. Due to the intense and repetitive nature of CrossFit, concerns have been raised over the potential injury risks that are associated with its training including rhabdomyolysis and musculoskeletal injuries. However, identification of risk factors for predicting injuries in CrossFit athletes has been limited by the absence of relevant big epidemiological studies. The main purpose of this paper is the identification of risk factors and the development of machine learning-based models using ensemble learning that can predict CrossFit injuries. To accomplish the aforementioned targets, a survey-based epidemiological study was conducted in Greece to collect data on musculoskeletal injuries in CrossFit practitioners. A Machine Learning (ML) pipeline was then implemented that involved data pre-processing, feature selection and well-known ML models. The performance of the proposed ML models was assessed using a comprehensive cross validation mechanism whereas a discussion on the nature of the selected features is also provided. An area under the curve (AUC) of 77.93% was achieved by the best ML model using ensemble learning (Adaboost) on the group of six selected risk factors. The effectiveness of the proposed approach was evaluated in a comparative analysis with respect to numerous performance metrics including accuracy, sensitivity, specificity, AUC and confusion matrices to confirm its clinical relevance. The results are the basis for the development of reliable tools for the prediction of injuries in CrossFit. View Full-Text
Keywords: CrossFit; prediction; ensemble learning; machine learning CrossFit; prediction; ensemble learning; machine learning
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MDPI and ACS Style

Moustakidis, S.; Siouras, A.; Vassis, K.; Misiris, I.; Papageorgiou, E.; Tsaopoulos, D. Prediction of Injuries in CrossFit Training: A Machine Learning Perspective. Algorithms 2022, 15, 77.

AMA Style

Moustakidis S, Siouras A, Vassis K, Misiris I, Papageorgiou E, Tsaopoulos D. Prediction of Injuries in CrossFit Training: A Machine Learning Perspective. Algorithms. 2022; 15(3):77.

Chicago/Turabian Style

Moustakidis, Serafeim, Athanasios Siouras, Konstantinos Vassis, Ioannis Misiris, Elpiniki Papageorgiou, and Dimitrios Tsaopoulos. 2022. "Prediction of Injuries in CrossFit Training: A Machine Learning Perspective" Algorithms 15, no. 3: 77.

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