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Elahe Allahyari1, Narges Roustaei2

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Summary

Identifying at-risk populations for mental health interventions is crucial. This study found that place of residence, education, age, gender, and employment significantly predict mental disorders, with a 99.2% accurate artificial neural network model.

Keywords:
AgeArtificial neural networkEducationEmployment statusGenderMental disorderPlace of residence

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Area of Science:

  • Psychiatry and Behavioral Sciences
  • Computational Neuroscience
  • Public Health

Background:

  • Mental disorders significantly impact individuals and society, necessitating early identification of vulnerable populations.
  • Understanding demographic and socioeconomic predictors is key to improving societal psychological well-being.

Purpose of the Study:

  • To investigate the predictive importance of gender, employment, education, place of residence, and age on mental disorders.
  • To develop and validate an artificial neural network (ANN) model for mental disorder prediction.

Main Methods:

  • Utilized multilayer feed-forward back-propagation neural networks with five inputs and 10 outputs.
  • Evaluated various algorithms and hidden layer neuron counts to find the optimal ANN with minimal sum of square errors.
  • Analyzed data from 380 individuals aged 10-82 years using SPSS software.

Main Results:

  • The optimal ANN model demonstrated high accuracy (99.2%) in predicting mental disorders.
  • Place of residence (34.08%), education (20.11%), age (18.93%), gender (14.55%), and employment (12.33%) were significant predictors.
  • The model effectively identified patterns between demographic/socioeconomic factors and mental disorder prevalence.

Conclusions:

  • Employed, rural, younger individuals with non-tertiary education represent a key demographic for targeted mental health interventions.
  • The findings support the use of ANN modeling for identifying at-risk populations and informing public health strategies.
  • Further research can refine predictive models to enhance mental health support and reduce societal psychological burden.