Feature Selection and Radial Basis Function Network for Parkinson Disease Classification
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...
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| 主要な著者: | , , , |
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| フォーマット: | 論文 |
| 言語: | English |
| 出版事項: |
Sulaimani Polytechnic University - SPU
2017
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| 主題: | |
| オンライン･アクセス: | http://umpir.ump.edu.my/id/eprint/20035/1/Feature%20Selectionand%20Radial%20Basis%20Function%20Network%20for%20ParkinsonDisease%20Classification.pdf http://umpir.ump.edu.my/id/eprint/20035/ http://kjar.spu.edu.iq/index.php/kjar/article/view/137 |
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| 要約: | 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. |
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