Publication

Prediction of Running Injuries from Training Load: a Machine Learning Approach.

Dijkhuis, T., Otter, R., Velthuijsen, H. & Lemmink, K. A. P. M. 10-Mar-2017 Prediction of Running Injuries from Training Load: a Machine Learning Approach..

Research output: Scientific - peer-reviewConference contribution

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
Original languageEnglish
Title of host publicationPrediction of Running Injuries from Training Load: a Machine Learning Approach.
StatePublished - 10-Mar-2017
EventeTELEMED 2017 - Nice, France
Duration: 19-Mar-201723-Jun-2017
Conference number: 2308-4359
https://www.iaria.org/conferences2017/eTELEMED17.html

Conference

ConferenceeTELEMED 2017
CountryFrance
CityNice
Period19/03/201723/06/2017
Internet address

Event

eTELEMED 2017

19/03/201723/06/2017

Nice, France

Event: Conference

    Keywords

  • Human Performance, Machine Learning, predictive analysis, load, injuries, monitoring, endurance athletes

View graph of relations

ID: 42713674