Original scientific paper
https://doi.org/10.31341/jios.44.2.9
Quantum-Inspired Evolutionary Algorithms for Neural Network Weight Distribution: A Classification Model for Parkinson's Disease
Srishti Sahni
orcid.org/0000-0002-1879-385X
; Maharaja Agrasen Institute of Technology, New Delhi, India
Vaibhav Aggarwal
; Maharaja Agrasen Institute of Technology, New Delhi, India
Ashish Khanna
; Faculty of Computer Science, Maharaja Agrasen Institute of Technology
Deepak Gupta
; Faculty of Computer Science, Maharaja Agrasen Institute of Technology
Siddhartha Bhattacharyya
orcid.org/0000-0003-0360-7919
; Faculty of Electrical Engineering and Computer Science VSB Technical
Abstract
Parkinson’s Disease is a degenerative neurological disorder with unknown origins, making it impossible to be cured or even diagnosed. The following article presents a Three-Layered Perceptron Neural Network model that is trained using a variety of evolutionary as well as quantum-inspired evolutionary algorithms for the classification of Parkinson's Disease. Optimization algorithms such as Particle Swarm Optimization, Artificial Bee Colony Algorithm and Bat Algorithm are studied along with their quantum-inspired counter-parts in order to identify the best suited algorithm for Neural Network Weight Distribution. The results show that the quantum-inspired evolutionary algorithms perform better under the given circumstances, with qABC offering the highest accuracy of about 92.3%. The presented model can be used not only for disease diagnosis but is also likely to find its applications in various other fields as well.
Keywords
Parkinson’s Disease; Particle Swarm Optimization; Artificial Bee Colony Algorithm; Bat Algorithm; Quantum Optimization; Neural Network Weight Distribution
Hrčak ID:
247574
URI
Publication date:
9.12.2020.
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