Fig. 2.

(left) A decision tree, with six leaves, for prediction of UPDRS-III motor outcome. (right) The performance of the tree on the data is shown, which is sub-optimal, given that only one of six outcomes can be arrived at, at the leaves. Use of random forest of decision trees aims to improve this performance. Radiomic features, such as difference Entropy, SZHGE and LZLGE as seen above, are elaborated in the supplement. (m) and (l) refer to the more and less affected sides, respectively (e.g. caudate(m) is the more affected caudate).