Application Of Radial Basis Function Network On Parkinson Data

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dc.contributor.authorNur Farahana Zainudin
dc.contributor.authorNorizan Mohamed
dc.contributor.authorNor Azlida Aleng
dc.contributor.authorSiti Hasliza Ahmad Rusmili
dc.date.accessioned2017-04-16T08:33:39Z
dc.date.available2017-04-16T08:33:39Z
dc.date.issued2015-10
dc.description.abstractRadial 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.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/5860
dc.language.isoenen_US
dc.publisherJurnal Teknologien_US
dc.subjectRadial Basis Function (RBFN)en_US
dc.subjectParkinson dataen_US
dc.subjectR2en_US
dc.titleApplication Of Radial Basis Function Network On Parkinson Dataen_US
dc.typeArticleen_US
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