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

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