Prediction of running injuries from training load: a machine learning approach

Vertaalde titel van de bijdrage: Voorspelling van hardloopblessures op basis van trainingsload: een machine learning benadering

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The prediction of the running injuries based on selfreported training data on load is difficult. At present, coaches and researchers have no validated system to predict if a runner has an increased risk of injuries. We aim to develop an algorithm to predict the increase of the risk of a runner to sustain an injury. As a first step Self-reported data on training parameters and injuries from high-level runners (duration=37 weeks, n=23, male=16, female=7) were used to identify the most predictive variables for injuries, and train a machine learning tree algorithm to predict an injury. The model was validated by splitting the data in training and a test set. The 10 most important variables were identified from 85 possible variables using the Random Forest algorithm. To predict at an earliest stage, so the runner or the coach is able to intervene, the variables were classified by time to build tree algorithms up to 7 weeks before the occurrence of an injury. By building machine learning algorithms using existing self-reported training data can enable prospective identification of high-level runners who are likely to develop an injury. Only the established prediction model needs to be verified as correct.
Vertaalde titel van de bijdrageVoorspelling van hardloopblessures op basis van trainingsload: een machine learning benadering
Originele taal-2English
Aantal pagina's3
StatusPublished - 19 mrt. 2017
EvenementThe Ninth International Conference on eHealth, Telemedicine, and Social Medicine - Nice, France
Duur: 19 mrt. 201723 mrt. 2017
Congresnummer: 9th
https://www.iaria.org/conferences2017/eTELEMED17.html

Conference

ConferenceThe Ninth International Conference on eHealth, Telemedicine, and Social Medicine
Verkorte titeleTELEMED 2017
Land/RegioFrance
StadNice
Periode19/03/1723/03/17
Internet adres

Keywords

  • sportblessures
  • trainingsanalyse

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