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Related Concept Videos

Social Anxiety Disorder01:28

Social Anxiety Disorder

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Social anxiety disorder, also known as social phobia, is characterized by an intense fear of social situations where one might face humiliation, rejection, embarrassment, or negative evaluation. This disorder leads individuals to avoid activities like casual conversations, public speaking, or seemingly simple tasks such as eating, signing documents, or swimming, in public settings. Its impact extends beyond discomfort, often significantly interfering with daily functioning and quality of life.
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Autism Spectrum Disorder01:19

Autism Spectrum Disorder

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Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by persistent deficits in social communication and interaction alongside restrictive and repetitive behaviors or interests. ASD is sometimes accompanied by intellectual impairment.
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Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
Participant Modeling
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Generalized Anxiety Disorder (GAD) is a chronic condition characterized by excessive and uncontrollable worry that persists for at least six months, significantly interfering with daily functioning. Unlike situational anxiety, which arises in response to specific stressors, GAD often occurs without a clear cause. Individuals may experience disproportionate worry about work, health, or relationships. For instance, a person might continuously fear poor health despite normal medical evaluations or...
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Behavior therapy incorporates diverse techniques rooted in classical conditioning principles to address maladaptive behaviors and anxiety disorders. These methods aim to reduce avoidance behaviors, foster adaptive coping mechanisms, and alter associations between stimuli and responses, making them effective in a wide range of therapeutic contexts.
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SADXAI: Predicting social anxiety disorder using multiple interpretable artificial intelligence techniques.

Krishnaraj Chadaga1, Srikanth Prabhu1, Niranjana Sampathila2

  • 1Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka 576104, India.

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|March 20, 2024
PubMed
Summary
This summary is machine-generated.

Social anxiety disorder (SAD) can be diagnosed using machine learning models analyzing patient symptoms. AdaBoost and logistic regression achieved 88% accuracy, identifying key diagnostic factors for early detection.

Keywords:
Artificial intelligenceClinical decision support systemDSM-5Explainable artificial intelligenceMachine learningSocial anxiety disorder (SAD)

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Area of Science:

  • Psychiatry and Computer Science
  • Application of Artificial Intelligence in Mental Health Diagnosis

Background:

  • Social anxiety disorder (SAD), or social phobia, involves persistent fear of negative judgment, impacting social and professional life.
  • SAD arises from a complex interplay of environmental and biological factors.
  • Diagnosis relies on criteria outlined in the Diagnostic and Statistical Manual of Mental Health Disorders (DSM-5), considering physical, emotional, and demographic symptoms.

Purpose of the Study:

  • To investigate the efficacy of machine learning (ML) techniques in diagnosing Social Anxiety Disorder (SAD).
  • To develop an interpretable diagnostic framework for SAD using eXplainable Artificial Intelligence (XAI).

Main Methods:

  • Utilized demographic, emotional, and physical symptom data for SAD diagnosis.
  • Applied multiple machine learning classifiers, including AdaBoost and logistic regression.
  • Employed four eXplainable Artificial Intelligence (XAI) techniques to interpret model predictions.

Main Results:

  • AdaBoost and logistic regression models achieved the highest diagnostic accuracy at 88%.
  • XAI analysis identified the "Liebowitz Social Anxiety Scale questionnaire" and "fear of speaking in public" as critical diagnostic attributes.
  • The study established a clinical decision support system framework for SAD identification.

Conclusions:

  • Machine learning models demonstrate high accuracy in diagnosing Social Anxiety Disorder.
  • XAI enhances the interpretability of ML-based SAD diagnosis, highlighting key contributing factors.
  • The developed framework has potential applications in educational, healthcare, and occupational settings for early SAD detection.