Title: An evolutionary framework on ADHD diagnosis based on graph theory and ant colony optimisation

Authors: R. Catherine Joy; S. Thomas George; A. Albert Rajan

Addresses: Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India ' Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India ' Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India

Abstract: Developing countries facing unavoidable issues for the parents living with children due to attention deficit hyperactivity disorder (ADHD). This neuropsychiatric disorder has effects on the children in terms of inattentive, impulsive, and hyperactivity. Graph theory provides useful description measures as predicted vectors for the classification process and this research work provides an automated diagnosis model for predicting the ADHD features based on the neural network classifier to differentiate ADHD patients and their healthy controls from a combined environment includes normal persons and affected patients. Ant colony optimisation model is used to get converged results for the classifier results in terms of both phenotypic data and imaging data. ADHD-200 dataset is used for analysis in the proposed model. The experimental result yields an accuracy of 86% on two class diagnosis better than phenotypic approaches.

Keywords: attention deficit hyperactivity disorder; ADHD; artificial neural network; ant colony optimisation.

DOI: 10.1504/IJCAET.2021.117132

International Journal of Computer Aided Engineering and Technology, 2021 Vol.15 No.2/3, pp.218 - 231

Received: 16 Oct 2018
Accepted: 03 Dec 2018

Published online: 19 Aug 2021 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article