دورية أكاديمية
Performance analysis of different classification algorithms using different feature selection methods on Parkinson's disease detection.
| العنوان: | Performance analysis of different classification algorithms using different feature selection methods on Parkinson's disease detection. |
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| المؤلفون: | Cigdem O; Department of Electrical and Electronics Engineering, Eastern Mediterranean University, Gazimagusa, Mersin 10, Turkey. Electronic address: ozkancigdem@ieee.org., Demirel H; Department of Electrical and Electronics Engineering, Eastern Mediterranean University, Gazimagusa, Mersin 10, Turkey. |
| المصدر: | Journal of neuroscience methods [J Neurosci Methods] 2018 Nov 01; Vol. 309, pp. 81-90. Date of Electronic Publication: 2018 Sep 01. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Elsevier/North-Holland Biomedical Press Country of Publication: Netherlands NLM ID: 7905558 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1872-678X (Electronic) Linking ISSN: 01650270 NLM ISO Abbreviation: J Neurosci Methods Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: Amsterdam, Elsevier/North-Holland Biomedical Press. |
| مواضيع طبية MeSH: | Magnetic Resonance Imaging*, Brain/*diagnostic imaging , Brain/*physiopathology , Diagnosis, Computer-Assisted/*methods , Parkinson Disease/*diagnosis, Adult ; Aged ; Algorithms ; Female ; Gray Matter/diagnostic imaging ; Gray Matter/physiopathology ; Humans ; Imaging, Three-Dimensional/methods ; Male ; Middle Aged ; Parkinson Disease/classification ; White Matter/diagnostic imaging ; White Matter/physiopathology |
| مستخلص: | Background: In diagnosis of neurodegenerative diseases, the three-dimensional magnetic resonance imaging (3D-MRI) has been heavily researched. Parkinson's disease (PD) is one of the most common neurodegenerative disorders. New Method: The performances of five different classification approaches using five different attribute rankings each followed with an adaptive Fisher stopping criteria feature selection (FS) method are evaluated. To improve the performance of PD detection, a source fusion technique which combines the gray matter (GM) and white (WM) tissue maps and a decision fusion technique which combines the outputs of all classifiers using the correlation-based feature selection (CFS) method by majority voting are used. Results: Among the five FS methods, the CFS provides the highest results for all five classification algorithms and the SVM provides the best classification performances for all five different FS methods. The classification accuracy of 77.50% and 81.25% are obtained for the GM and WM tissues, respectively. However, the fusion of GM and WM datasets improves the classification accuracy of the proposed methodology up to 95.00%. Comparison With Existing Methods: An f-contrast is used to generate 3D masks for GM and WM datasets and a fusion technique, combining the GM and WM datasets is used. Several classification algorithms using several FS methods are performed and a decision fusion technique is used. Conclusions: Using the combination of the 3D masked GM and WM tissue maps and the fusion of the outputs of multiple classifiers with CFS method gives the classification accuracy of 95.00%. (Copyright © 2018 Elsevier B.V. All rights reserved.) |
| فهرسة مساهمة: | Keywords: DARTEL; Decision fusion; Feature selection; Parkinson's disease; Source fusion; Structural MRI |
| تواريخ الأحداث: | Date Created: 20180904 Date Completed: 20191104 Latest Revision: 20191104 |
| رمز التحديث: | 20221213 |
| DOI: | 10.1016/j.jneumeth.2018.08.017 |
| PMID: | 30176256 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 1872-678X |
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| DOI: | 10.1016/j.jneumeth.2018.08.017 |