Multiclass classification of Parkinson’s disease using different classifiers and LLBFS feature selection algorithm
| Autor: | Jamal Elmhamdi, Elmehdi Benmalek, Abdelilah Jilbab |
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| Rok vydání: | 2017 |
| Předmět: |
Linguistics and Language
Parkinson's disease Computer science 0206 medical engineering Feature selection 02 engineering and technology Machine learning computer.software_genre Language and Linguistics Voice analysis Multiclass classification Rating scale Healthy control 0202 electrical engineering electronic engineering information engineering medicine business.industry Pattern recognition Speech processing medicine.disease 020601 biomedical engineering Human-Computer Interaction 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Artificial intelligence business computer Software Subspace topology |
| Zdroj: | International Journal of Speech Technology. 20:179-184 |
| ISSN: | 1572-8110 1381-2416 |
| Popis: | Since approximately 90% of the people with PD (Parkinson's disease) suffer from speech disorders including disorders of laryngeal, respiratory and articulatory function, using voice analysis disease can be diagnosed remotely at an early stage with more reliability and in an economic way. All previous works are done to distinguish healthy people from people with Parkinson's disease (PWP). In this paper, we propose to go further by multiclass classification with three classes of Parkinson stages and healthy control. So we have used 40 features dataset, all the features are analyzed and 9 features are selected to classify PWP subjects in four classes, based on unified Parkinson's disease Rating Scale (UPDRS). Various classifiers are used and their comparison is done to find out which one gives the best results. Results show that the subspace discriminant reach more than 93% overall classification accuracy. |
| Databáze: | OpenAIRE |
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