Machine learning in knee injury sequelae detection: Unravelling the role of psychological factors and preventing long-term sequelae.

Purpose: This study evaluated the performance of three machine learning (ML) algorithms-decision tree (DT), multilayer perceptron (MLP) and extreme gradient boosting (XGB)-in identifying regular athletes who suffered a knee injury several months to years prior. In addition, the contribution of psychological variables in addition to biomechanical ones in the classification performance of the ML algorithms was assessed, to better identify factors to get back to competitive sport with the lowest possible risk of new knee injury.

Methods: A cohort of 96 athletes, 36 with prior knee injuries, practicing an average of 5.