Feature Selection and Radial Basis Function Network for Parkinson Disease Classification

Ibrahim, Ashraf Osman and Hussien, Walaa Akif and Yagoop, Ayat Mohammoud and Mohd Arfian, Ismail (2017) Feature Selection and Radial Basis Function Network for Parkinson Disease Classification. Kurdistan Journal of Applied Research, 2 (3). pp. 167-171. ISSN 2411-7706. (Published)

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Recently, several works have focused on detection of a different disease using computational intelligence techniques. In this paper, we applied feature selection method and radial basis function neural network (RBFN) to classify the diagnosis of Parkinson’s disease. The feature selection (FS) method used to reduce the number of attributes in Parkinson disease data. The Parkinson disease dataset is acquired from UCI repository of large well-known data sets. The experimental results have revealed significant improvement to detect Parkinson’s disease using feature selection method and RBF network.

Item Type: Article
Uncontrolled Keywords: Parkinson’s disease, feature selection, artificial neural networks, classification, radial basis function, attributes reduction
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty/Division: Faculty of Computer System And Software Engineering
Depositing User: Dr. Mohd Arfian Ismail
Date Deposited: 29 Jan 2018 06:21
Last Modified: 29 Jan 2018 06:21
URI: http://umpir.ump.edu.my/id/eprint/20035
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