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Application of artificial neural network in predicting EI.

Elahe Allahyari1

  • 1Social Determinants of Health Research Center, Faculty of Health, Department of Epidemiology and Biostatistics, Birjand University of Medical Sciences, Birjand, Iran.

Biomedicine
|April 15, 2021
PubMed
Summary

Artificial neural networks effectively predict emotional intelligence (EI) using sociological variables, outperforming traditional regression models. This approach aids in understanding EI variations across different demographic groups.

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Area of Science:

  • Psychology
  • Data Science

Background:

  • Emotional intelligence (EI) encompasses non-cognitive skills vital for managing environmental demands.
  • Factors like gender, age, education, and occupation influence EI, but their complex interactions are challenging to model.
  • Traditional regression models face limitations due to restrictive assumptions when analyzing multifaceted behavioral phenomena.

Purpose of the Study:

  • To explore artificial neural networks (ANNs) as a superior alternative to regression models for predicting EI.
  • To identify patterns in EI based on sociological variables such as age, gender, occupation, marital status, and education.

Main Methods:

  • Utilized SPSS software to implement an artificial neural network model.
  • Analyzed data from 901 individuals aged 17 to 73 years.
Keywords:
Artificial neural networksEmotional intelligenceSociological variables

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  • Employed a specific ANN architecture: a hyperbolic tangent transfer function in the hidden layer (two neurons) and a sigmoid transfer function in the output layer.

    • Main Results:

    • The developed ANN model demonstrated significant correlations in predicting most EI dimensions.
    • The neural network model proved advantageous over regression models for EI prediction using sociological variables.
    • Identified an optimal ANN structure for EI prediction.

    Conclusions:

    • The ANN model accurately estimates EI levels across diverse occupational, educational, gender, and age groups.
    • This predictive capability provides a foundation for targeted interventions to address EI deficiencies within specific demographics.
    • Highlights the utility of ANNs in behavioral science research for complex variable interactions.