Quantum-Inspired Evolutionary Algorithms for Neural Network Weight Distribution: A Classification Model for Parkinson's Disease

A Classification Model for Parkinson's Disease

Authors

  • Srishti Sahni Maharaja Agrasen Institute of Technology
  • Vaibhav Aggarwal Maharaja Agrasen Institute of Technology
  • Ashish Khanna Faculty of Computer Science, Maharaja Agrasen Institute of Technology
  • Deepak Gupta Faculty of Computer Science, Maharaja Agrasen Institute of Technology
  • Siddhartha Bhattacharyya Faculty of Electrical Engineering and Computer Science, VSB Technical

DOI:

https://doi.org/10.31341/jios.44.2.9

Keywords:

Parkinson’s Disease, Particle Swarm Optimization, Artificial Bee Colony Algorithm, Bat Algorithm, Quantum Optimization, Neural Network Weight Distribution

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.

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Published

2020-12-08

How to Cite

[1]
S. Sahni, V. Aggarwal, A. Khanna, D. Gupta, and S. Bhattacharyya, “Quantum-Inspired Evolutionary Algorithms for Neural Network Weight Distribution: A Classification Model for Parkinson’s Disease: A Classification Model for Parkinson’s Disease”, J. inf. organ. sci. (Online), vol. 44, no. 2, Dec. 2020.

Issue

Vol. 44 No. 2 (2020)

Section

Articles