Feature Selection and Radial Basis Function Network for Parkinson Disease Classification


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  • Ashraf Osman Ibrahim Faculty of Computer Science, Future University, Khartoum, Sudan Arab Open University, Sudan
  • Walaa Akif Hussien Faculty of Computer Science, Future University, Khartoum, Sudan
  • Ayat Mohammoud Yagoop Faculty of Computer Science, Future University, Khartoum, Sudan
  • Mohd Arfian Ismail Soft Computing & Intelligent Systems Research Group, Faculty of Computer System and Software Engineering, University Malaysia Pahang, Pahang, Malaysia


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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How to Cite

A. O. Ibrahim, W. A. Hussien, A. M. Yagoop, and M. A. Ismail, “Feature Selection and Radial Basis Function Network for Parkinson Disease Classification”, KJAR, vol. 2, no. 3, pp. 167–171, Aug. 2017, doi: 10.24017/science.2017.3.121.

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