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. Author manuscript; available in PMC: 2018 Jan 15.
Published in final edited form as: Neuroimage. 2016 Feb 13;145(Pt B):254–264. doi: 10.1016/j.neuroimage.2016.02.016

Figure 4.

Figure 4

A) Confusion matrix and ROC curve generated from a LASSO algorithm which was trained using ‘raw’ neurocognitive tasks scores (see Table 2) to predict individual subjects phenotypic labels. The model predicted individual subjects phenotypic labels with 94% accuracy (sensitivity= 92%, specificity =97%) and area under ROC curve (AUC) = 0.9449. B) Confusion matrix and ROC curve generated from a Elastic Net algorithm which was trained using whole brain fractional anisotropy values to predict individual subjects phenotypic labels. The model predicted individual subjects phenotypic labels with 76% accuracy (sensitivity= 76%, specificity =76%) and area under ROC curve (AUC) = 0.7593.