Machine learning-based diagnosis support system for differentiating between clinical anxiety and depression disorders

Thalia Richter, Barak Fishbain, Eyal Fruchter, Gal Richter-Levin, Hadas Okon-Singer

Research output: Contribution to journalArticlepeer-review


In light of the need for objective mechanism-based diagnostic tools, the current research describes a novel diagnostic support system aimed to differentiate between anxiety and depression disorders in a clinical sample. Eighty-six psychiatric patients with clinical anxiety and/or depression were recruited from a public hospital and assigned to one of the experimental groups: Depression, Anxiety, or Mixed. The control group included 25 participants with no psychiatric diagnosis. Participants performed a battery of six cognitive-behavioral tasks assessing biases of attention, expectancies, memory, interpretation and executive functions. Data were analyzed with a machine-learning (ML) random forest-based algorithm and cross-validation techniques. The model assigned participants to clinical groups based solely on their aggregated cognitive performance. By detecting each group's unique performance pattern and the specific measures contributing to the prediction, the ML algorithm predicted diagnosis classification in two models: (I) anxiety/depression/mixed vs. control (76.81% specificity, 69.66% sensitivity), and (II) anxiety group vs. depression group (80.50% and 66.46% success rates in classifying anxiety and depression, respectively). The findings demonstrate that the cognitive battery can be utilized as a support system for psychiatric diagnosis alongside the clinical interview. This implicit tool, which is not based on self-report, is expected to enable the clinician to achieve increased diagnostic specificity and precision. Further, this tool may increase the confidence of both clinician and patient in the diagnosis by equipping them with an objective assessment tool. Finally, the battery provides a profile of biased cognitions that characterizes the patient, which in turn enables more fine-tuned, individually-tailored therapy.

Original languageEnglish
Pages (from-to)199-205
Number of pages7
JournalJournal of Psychiatric Research
StatePublished - Sep 2021

Bibliographical note

Publisher Copyright:
© 2021 The Authors


  • Anxiety
  • Cognitive bias
  • Depression
  • Diagnostic support system
  • Machine learning
  • Psychiatric diagnosis

ASJC Scopus subject areas

  • Psychiatry and Mental health
  • Biological Psychiatry


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