Designing an accurate system for differentiating children with attention deficit-hyperactivity disorder from oppositional defiant disorder by using artificial neural network

Document Type : Original article

Authors

1 1. Ph.D Student of Exceptional child psychology, Depatment of Psychology and Education, University of Tehran, Tehran, Iran

2 2. Department of Psychology, University of Tehran, Tehran, Iran.

3 3. Associated Professor, Psychiatrist, Faculty of Rehabilitation Scienses, Shahid Beheshti University of Medical Scienses, Tehran, Iran.

4 4. Distinguished Professor, Department of Psychology and Education, University of Tehran, Tehran, Iran

5 5. Associated Professor, Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran.

6 6. Professor, Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran.

Abstract

Background and Aim: Behavioral disorders are one of the most considerable disorders in children during these days. Accurate diagnosis and early identification, especially in disorders with similar sysmptoms, are noticeable and very important. Moreover, most of the affected individuals are rejected by their parents and teachers, decreasing their chances of normal development and their future life will be affected.
Due to many similarities among oppositional defiant disorder and attention deficit-hyperactivity disorder, differentiation of these disorders is challenging, although diagnosing and distinguishing of these disorders are very important.
Materials and Methods: Due to overlapping between oppositional defiant disorder and attention deficit hyperactivity disorder and normal behavior with temporarely aggression, it was tried to design an artificial neural network to assist in accurate distinguishing these classes.
Samples were consisted of 85 children with behavioral disorders (including ADHD and oppositional defiant disorder) and 50 children with normal behavior but temporarly sign of aggresion. Multilayer perceptron neural network was used to designe the system.
Results: The average of accuracy of correct classification with the desined network reacehed to 95.55%. The designed system can be used as a reliable assistant for the psychiatrists and it can increase the diagnosis realiability.
Conclusion: The designed system can differentiate children with behavioral disorders with high accuracy. It can be used as a screening tool for high risk children.

Keywords

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The Scientific Journal of Rehabilitation Medicine
Volume 4, Issue 1 - Serial Number 1
March and April 2015
Pages 90-98

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History

  • Receive Date: 09 March 2014
  • Revise Date: 22 June 2014
  • Accept Date: 27 October 2014
  • First Publish Date: 21 March 2015

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