Fig. 2 | Scientific Reports

Fig. 2

From: Precise disease heterogeneity and progression quantification in MSA and Parkinson’s disease using machine learning

Fig. 2

Classification performance shown as receiver operator characteristic plot (ROC) with the area under the curve (AUC) of the heterogeneity (HET) score derived from machine learning models trained on volume, fractional anisotropy (FA), and mean diffusivity (MD). Each HET score is compared to the cerebellum (Cb) white matter (WM) volume (vol), fractional anisotropy (FA), and mean diffusivity (MD), and the MSA-atrophy index (AI) at baseline and follow-up visits.

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