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An effective and novel wavelet neural network approach in classifying type 2 diabetics

Zainuddin, Zarita and Ong, Pauline (2012) An effective and novel wavelet neural network approach in classifying type 2 diabetics. Neural Network World, 22 (5). pp. 407-428. ISSN 23364335

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Designing a wavelet neural network (WNN) needs to be done judiciously in attaining the optimal generalization performance. Its prediction competence relies highly on the initial value of translation vectors. However, there is no established solution in determining the appropriate initial value for the translation vectors at this moment. In this paper, we propose a novel enhanced fuzzy c-means clustering algorithm – specifically, the modified point symmetry-based fuzzy c-means (MPSDFCM) algorithm – in initializing the translation vectors of the WNNs. The effectiveness of embedding different activation functions in WNNs will be investigated as well. The categorization effectiveness of the proposed WNNs model was then evaluated in classifying the type 2 diabetics, and was compared with the multilayer perceptrons (MLPs) and radial basis function neural networks (RBFNNs) models. Performance assessment shows that our proposed model outperforms the rest, since a 100% superior classification rate was achieved.

Item Type: Article
Uncontrolled Keywords: Clustering; diabetes; fuzzy c-means; microarray; wavelet neural network
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: Faculty of Mechanical and Manufacturing Engineering > Department of Engineering Mechanics
Depositing User: Mr. Mohammad Shaifulrip Ithnin
Date Deposited: 20 Feb 2019 08:13
Last Modified: 20 Feb 2019 08:13
URI: http://eprints.uthm.edu.my/id/eprint/10759
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