Multiclass classification of Parkinson’s disease using different classifiers and LLBFS feature selection algorithm

Author:

Benmalek ElmehdiORCID,Elmhamdi Jamal,Jilbab Abdelilah

Publisher

Springer Science and Business Media LLC

Subject

Computer Vision and Pattern Recognition,Linguistics and Language,Human-Computer Interaction,Language and Linguistics,Software

Reference21 articles.

1. Baken R.J. & Orlikoff R.F. (2000). Clinical measurement of speech and voice (2nd ed.). San Diego: Singular Thomson Learning

2. Chen, H.-L., Huang, C.-C., Yu, X.-G., Xu, X., Sun, X., Wang, G., & Wang, S. (2013). An efficient diagnosis system for detection of Parkinson’s disease using fuzzy k-nearest neighbor approach. Expert Systems with Applications, 40(1), 263–271.

3. Duffy, & Motor, R. J. (2005). Speech Disorders: Substrates, Differential Diagnosis and Management. (2nd ed.). St. Louis: Elsevier Mosby

4. ENE MARIUS (2008). Neural network-based approach to discriminate healthy people from those with Parkinson’s disease. Annals of the University of Craiova, 35, 112–116.

5. Fraile, R., Saenz-Lechon, N., Godino-Llorente, J. I., Osma-Ruiz, V., & Fredouille, C. (2009). Automatic detection of laryngeal pathologies in records of sustained vowels by means of mel-frequency cepstral coefficient parameters and differentiation of patients by sex. Folia Phoniatrica et Logopaedica, 61, 146–152.

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