Feature Selection and Radial Basis Function Network for Parkinson Disease Classification

Abstract = 56 times | PDF = 35 times


Ashraf Osman Ibrahim Walaa Akif Hussien Ayat Mohammoud Yagoop Mohd Arfian Ismail


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.


Parkinson’s disease, feature selection, artificial neural networks, classification, radial basis function, attributes reduction.


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