Although the displacement of the points plays an important role in finding the regression, the used clinical features contribute in adjusting the amount of prediction required for each point of healthy or damaged region. The construction of trees in RF allows to report the importance of each feature. In this regard, clinical studies can be accompanied with the resulted features’ importance to highlight the ones which contribute more in the prediction. Based on the conducted analysis, the clinical features applied in the training procedure are studied to determine their level of importance to construct the regressor. Table 9 denotes the importance of features in percentage, for the regressors trained in each of the three axes. Additionally, for the features belonging to the same group, not only the individual importance, but also the grouped importance (average of individuals) is reported, as well.