A comparative study of feature projection and feature selection approaches for Parkinson's disease detection and classification using T1-weighted MRI scans Online publication date: Tue, 15-Feb-2022
by Gunjan Pahuja; T.N. Nagabhushan; Bhanu Prasad
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 38, No. 1, 2022
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.
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