Abstract
In order to lessen gait deviations, single-event multilevel surgery (SEMLS) is usually performed on patients with cerebral palsy. The objective of this work is to utilize statistical machine learning techniques to simulate the effect of these combinations of surgical procedures on kinematics. Thereby, it would be possible to predict postoperative gait of children with CP, knowing the preoperative state and the surgical program
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