  Facial dysmorphology or malocclusion is frequently associated with abnormal
growth of the face. The ability to predict facial growth (FG) direction would
allow clinicians to prepare individualized therapy to increase the chance for
successful treatment. Prediction of FG direction is a novel problem in the
machine learning (ML) domain. In this paper, we perform feature selection and
point the attribute that plays a central role in the abovementioned problem.
Then we successfully apply data augmentation (DA) methods and improve the
previously reported classification accuracy by 2.81%. Finally, we present the
results of two experienced clinicians that were asked to solve a similar task
to ours and show how tough is solving this problem for human experts.
