Email this article Comparing machine learning and conventional statistical approaches for injury prediction in young professional soccer players
Abstract
with preventive measures. Consequently, injury prediction and prevention
are also increasingly addressed from a statistical perspective. In a pilot
study, several machine learning algorithms and conventional statistical approaches
have been compared regarding their potential to predict time-loss
non-contact lower-body injuries in professional youth soccer players, using
data from a prospective cohort study with 56 players of which 22 were injured.
The covariates considered here include basic soccer-related as well
as neuromuscular and biomechanical features derived from physical testing.
Lasso regularized logistic regression, naive Bayes, linear discriminant analysis,
k-nearest neighbors, classification trees, random forests, XGBoost, and
support vector machines are considered for binary classification and prediction
of an injury occurrence. The prediction results from a cross-validated
procedure are compared regarding multiple quality measures. Post Lasso
logistic regression with a reduced penalty gives the best results with an accuracy
of 0.625, a predictive likelihood of 0.593, and a Brier score of 0.228.
The respective sensitivity and specificity are 0.773 and 0.529, with an AUC of
0.672. Three features have been identified to be of particular relevance, the
concentric extensor peak torque of the knee, the transversal plane moment
of the hip in a single-leg drop landing task, and the sway in postural control
under static conditions. Moreover, an XGBoost model which primarily uses the two first-mentioned covariates slightly outperforms the Lasso model in
terms of accuracy (0.661), while for the other performance measures it is
dominated by the Lasso.
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