Parkinson's diagnosis using ant-lion optimisation algorithm. (30th October 2019)

• Record Type: Journal Article
• Authors: Sharma, Prerna; Jain, Rishabh; Sharma, Moolchand; Gupta, Deepak
• Is Part Of: International journal of innovative computing and applications. Volume 10:Number 3/4(2019)
• Page Start: 138
• Page End: 146
• Publication Date: 2019-10-30
• Subjects: ant-lion optimisation -- ALO -- feature extraction -- bio-inspired algorithm -- Parkinson's disease; Evolutionary computation -- Periodicals; Neural networks (Computer science) -- Periodicals; Genetic programming (Computer science) -- Periodicals; Biologically-inspired computing -- Periodicals; Swarm intelligence -- Periodicals; Quantum computers -- Periodicals; 006.3
• Languages: English
• ISSNs: 1751-648X

Abstract

Parkinson's disease (PD) is a long term progressive disorder of the central nervous system that mainly affects the movement of the body. But there are several limitations in detecting PD at an early stage. In this paper, a binary variant of the recently proposed ant-lion optimisation (ALO) algorithm has been proposed and implemented for diagnosing patients for Parkinson's disease at early stages. ALO is a recently proposed bio-inspired algorithm, which imitates the hunting patterns of ant-lions or doodlebugs proposed algorithm is used to find a minimum number of features that result in higher accuracy using machine learning classifiers. The proposed modified version of ALO extracts the optimal features for the two different Parkinson's Datasets with improved accuracy and computational time. The maximum accuracy achieved by the classifiers after optimal feature selection is 95.91%. The proposed algorithm results have been compared with other related algorithms for the same datasets.