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Anxiety in young people: Analysis from a machine learning model.

Marcela Tabares Tabares1, Consuelo Vélez Álvarez2, Joshua Bernal Salcedo3

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Artificial intelligence effectively detects anxiety in young people using machine learning models. The Random Forest model achieved 91% accuracy, highlighting the importance of social and family factors in mental health.

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Area of Science:

  • Mental Health
  • Artificial Intelligence
  • Machine Learning

Background:

  • Anxiety symptoms in young people require early detection for effective intervention.
  • Standardized questionnaires like PHQ-9 and GAD-7 are crucial for data collection.

Purpose of the Study:

  • To evaluate the efficacy of AI models in detecting anxiety symptoms in adolescents.
  • To identify key predictors of anxiety in the youth population.

Main Methods:

  • Utilized Support Vector Machine (SVM), K Nearest Neighbors (KNN), and Random Forest (RF) machine learning models.
  • Employed cross-validation techniques to assess model performance and accuracy.
  • Collected data using Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder 7-item scale (GAD-7).

Main Results:

  • The Random Forest model demonstrated the highest efficiency with 91% accuracy, outperforming previous studies.
  • Identified significant anxiety predictors including parental education, alcohol consumption, and social security affiliation.
  • Observed correlations between anxiety and personal/family history of mental illness and depression.

Conclusions:

  • AI, particularly the RF model, shows significant promise for early anxiety detection in young individuals.
  • Emphasizes the need to consider multidimensional factors (social, family, clinical) in anxiety assessment and treatment.
  • Recommends expanding sample size in future research to enhance model robustness.