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Title: A comparative study of feature projection and feature selection approaches for Parkinson's disease detection and classification using T1-weighted MRI scans

Authors: Gunjan Pahuja; T.N. Nagabhushan; Bhanu Prasad

Addresses: Department of Computer Science and Engineering, JSSATE Noida, Dr. A.P.J Abdul Kalam Technical University, Uttar Pradesh, India ' Department of Information Science and Engineering, Sri Jayachamarajendra College of Engineering, Mysore, India ' Department of Computer and Information Sciences, Florida A&M University, Tallahassee, Florida 32307, USA

Abstract: In this research, a multivariate analysis between feature projection and feature subset selection methods has been performed with the objective of identifying a subset of features that would help in detection and classification of people affected by Parkinson's disease. For this study, T1-weighted MRI data has been collected from Parkinson's Progression Markers Initiative organisation. The accuracy of support vector machine classifier has been checked with different number of selected features during the exploratory phase. The obtained results have shown a clear potential for using these methods in detecting and classifying the Parkinson's patients from normal persons. Further, to identify the brain region responsible for this disease, these selected features are mapped back to the standard Monteal Neurological Institute (MNI) template. ANOVA test has been employed to show the statistical significance of the obtained results.

Keywords: Parkinson's disease; PD; voxel-based morphometry; VBM; genetic algorithm; GA; eigenvector centrality-based discriminant analysis; ECDA; support vector machine; SVM; analysis of variance; ANOVA.

DOI: 10.1504/IJBET.2022.120863

International Journal of Biomedical Engineering and Technology, 2022 Vol.38 No.1, pp.65 - 80

Received: 29 Jun 2018
Accepted: 03 Oct 2018

Published online: 15 Feb 2022 *

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