A Survey for identifying Parkinson’s disease by Binary Bat Algorithm

S. JabeenBegum¹ , B. Swaathi²

Section:Survey Paper, Product Type: Isroset-Journal
Vol.7 , Issue.2 , pp.17-23, Apr-2019

CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v7i2.1723

Online published on Apr 30, 2019

Copyright © S. JabeenBegum, B. Swaathi . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Parkinson’s disease is a chronic neurological disorder that directly affects human gait. It leads to slowness of movement, causes muscle rigidity tremors. Analyzing human gait serves to be useful in studies aiming at early recognition of the disease. In the present work, we perform a comparative analysis of various nature-inspired algorithms to select optimal features/variables required for aiding in the classification of affected patients from the rest. Binary Bat Algorithm (BBA) has searched the feature space for optimal feature combinations with good accuracy. Bats use echolocation to detect prey and avoid obstacles in the dark by emitting ultrasound waves and listening to the echo produced through the waves reflecting from the surrounding objects. The accuracy can be made possible through various classification techniques such as SVM, NB, kNN.

Key-Words / Index Term :
Parkinson’sDisease(PD),Echolocation,SupportVectorMachine(SVM),NaïveBayesian(NB),k-Nearest Neighbor(kNN).

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