Using machine learning-based analysis for behavioral differentiation between anxiety and depression.

ABSTRACT: Anxiety and depression are distinct-albeit overlapping-psychiatric diseases, currently diagnosed by self-reported-symptoms. This research presents a new diagnostic methodology, which tests rigorously for differences in cognitive biases among subclinical anxious and depressed individuals. 125 participants were divided into four groups based on the levels of their anxiety and depression symptoms. A comprehensive behavioral test battery detected and quantified various cognitive-emotional biases. Advanced machine-learning tools, developed for this study, analyzed these results. These tools detect unique patterns that characterize anxiety versus depression to predict group membership. The prediction model for differentiating between symptomatic participants (i.e., high symptoms of depression, a

SUBMITTER: Richter T 

PROVIDER: S-EPMC7532220 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature