Fig. 4

Receiver Operator Curves for Model Classification Test. A bootstrap classification model was executed using a linear discriminant analysis (LDA) guided by a machine-learning algorithm. a A test group comprising young children (approx. 4 years old; n = 11) was used with discriminant scores from each LDA normalized to a center point of 5. The majority of model “votes” either < 5 or > 5 was used to classify each sample. Green and red marks indicate correct and incorrect identifications, respectively. b Receiver operator characteristic (ROC) curves for an iterative theoretical yield (blue dashed line) and the actual yield from the classification tests of the 1–5 yo group (green line). Here, overall accuracy was 73% with a sensitivity of 100%, specificity of 40%, and an area under the curve (AUC) of 0.691. The performance of this dynamic classification analysis suggests that there is high discrimination power that could be developed for diagnostic detection of spastic CP