TY - JOUR
T1 - Using machine learning-based analysis for behavioral differentiation between anxiety and depression
AU - Richter, Thalia
AU - Fishbain, Barak
AU - Markus, Andrey
AU - Richter-Levin, Gal
AU - Okon-Singer, Hadas
N1 - Publisher Copyright:
© 2020, The Author(s).
PY - 2020/10/2
Y1 - 2020/10/2
N2 - 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, anxiety, or both) compared to the non-symptomatic control group revealed a 71.44% prediction accuracy for the former (sensitivity) and 70.78% for the latter (specificity). 68.07% and 74.18% prediction accuracy was obtained for a two-group model with high depression/anxiety, respectively. The analysis also disclosed which specific behavioral measures contributed to the prediction, pointing to key cognitive mechanisms in anxiety versus depression. These results lay the ground for improved diagnostic instruments and more effective and focused individually-based treatment.
AB - 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, anxiety, or both) compared to the non-symptomatic control group revealed a 71.44% prediction accuracy for the former (sensitivity) and 70.78% for the latter (specificity). 68.07% and 74.18% prediction accuracy was obtained for a two-group model with high depression/anxiety, respectively. The analysis also disclosed which specific behavioral measures contributed to the prediction, pointing to key cognitive mechanisms in anxiety versus depression. These results lay the ground for improved diagnostic instruments and more effective and focused individually-based treatment.
UR - http://www.scopus.com/inward/record.url?scp=85091820386&partnerID=8YFLogxK
U2 - 10.1038/s41598-020-72289-9
DO - 10.1038/s41598-020-72289-9
M3 - Article
C2 - 33009424
AN - SCOPUS:85091820386
SN - 2045-2322
VL - 10
JO - Scientific Reports
JF - Scientific Reports
IS - 1
M1 - 16381
ER -