Please use this identifier to cite or link to this item: http://umt-ir.umt.edu.my:8080/handle/123456789/5860
Title: Application Of Radial Basis Function Network On Parkinson Data
Authors: Nur Farahana Zainudin
Norizan Mohamed
Nor Azlida Aleng
Siti Hasliza Ahmad Rusmili
Keywords: Radial Basis Function (RBFN)
Parkinson data
R2
Issue Date: Oct-2015
Publisher: Jurnal Teknologi
Abstract: Radial basis function networks have many uses, including the function approximation, time series production, classification and system control. Radial basis function based diagnosis of medical diseases has been taken into great consideration in recent studies. The real data from UCI Machine Learning websites that used 500 Parkinson’s patients and 7 different attributes as the subject were analyzed by using Statistical Package for Social Sciences (SPSS) 21.0. Next, the result of SPSS software will be used and run by MATLAB software. From the research that has been done by other researchers, it was found that MATLAB software is much better in producing the best results for Radial Basis Function. The value of R2 for Multiple Linear Regression and Radial Basis Function is 0.7450 and 0.9702 respectively. Hence, the Radial Basis Function method shows that there is more variability is explained by this model.
URI: http://hdl.handle.net/123456789/5860
Appears in Collections:Journal Articles

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