Feature Selection for Identifying Parkinson’s Disease Using Binary Grey Wolf Optimization

11 Pages Posted: 7 Mar 2018

See all articles by R.R Rajalaxmi

R.R Rajalaxmi

Kongu Engineering College, Perundurai

S Kaavya

Kongu Engineering College, Perundurai

Date Written: November 15, 2017

Abstract

Parkinson’s disease is a neurological disorder that directly affects human gait. It leads to slowness of movement, cannot speak properly and muscle rigidity. In this paper we perform a comparative analysis of various bio-inspired algorithms to select optimal feature. The proposed Binary Grey Wolf Optimization method is compared with four common optimizers namely Particle Swarm Optimization (PSO),Genetic Algorithm (GA), Binary Bat Algorithm (BBA) and Modified Cuckoo Search Algorithm (MCS). Accuracy and number of features selected are used to evaluate and compared with speech dataset from the UCI repository. Results indicate that the proposed binary version of grey wolf optimization (BGWO) has searched the feature space for optimal feature combinations with good accuracy.

Keywords: Parkinson’s disease, Feature selection, Binary grey wolf optimization algorithm

Suggested Citation

Rajalaxmi, R.R and Kaavya, S, Feature Selection for Identifying Parkinson’s Disease Using Binary Grey Wolf Optimization (November 15, 2017). Proceedings of the International Conference on Intelligent Computing Systems (ICICS 2017 – Dec 15th - 16th 2017) organized by Sona College of Technology, Salem, Tamilnadu, India, Available at SSRN: https://ssrn.com/abstract=3131662 or http://dx.doi.org/10.2139/ssrn.3131662

R.R Rajalaxmi (Contact Author)

Kongu Engineering College, Perundurai ( email )

Perundurai
638052
India

S Kaavya

Kongu Engineering College, Perundurai ( email )

Perundurai
638052
India

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