  After performing comparison of the performance of seven different machine
learning models on detection depression tasks to show that the choice of
features is essential, we compare our methods and results with the published
work of other researchers. In the end we summarize optimal practices in order
that this useful classification solution can be translated to clinical practice
with high accuracy and better acceptance.
