Comparison of machine classification algorithms for XXXX : neuroimages versus self-report.

@entity1 @entity239 @entity308 @entity148 @entity158 @entity1118
Recent studies have posited that machine learning (ML) techniques accurately classify individuals with and without @entity158 solely based on neuroimaging data. These studies claim that self-report is unreliable, making "objective" neuroimaging classification methods imperative. However, the relative performance of ML on neuroimaging and self-report data have not been compared. This study used commonly reported ML algorithms to measure differences between "objective" neuroimaging data and "subjective" self-report (ie, mood and @entity158 intensity) in their ability to discriminate between individuals with and without @entity1118 . Structural magnetic resonance imaging data from 26 individuals (14 individuals with @entity239 and 12 healthy controls) were processed to derive volumes from 56 brain regions per @entity1 . Self-report data included visual analog scale ratings for @entity158 intensity and mood (ie, anger, @entity148 , @entity308 , frustration, and fear). Separate models representing brain volumes, mood ratings, and @entity158 intensity ratings were estimated across several ML algorithms. Classification accuracy of brain volumes ranged from 53 to 76%, whereas mood and @entity158 intensity ratings ranged from 79 to 96% and 83 to 96%, respectively. Overall, models derived from self-report data outperformed neuroimaging models by an average of 22%. Although neuroimaging clearly provides useful insights for understanding neural mechanisms underlying @entity158 processing, self-report is reliable and accurate and continues to be clinically vital. PERSPECTIVE: The present study compares neuroimaging, self-reported mood, and self-reported @entity158 intensity data in their ability to classify individuals with and without @entity239 using ML algorithms. Overall, models derived from self-reported mood and @entity158 intensity data outperformed structural neuroimaging models.
