- Title
- Evolutionary wavelet neural network ensembles for breast cancer and Parkinson's disease prediction
- Creator
- Khan, Maryam Mahsal; Mendes, Alexandre; Chalup, Stephan K.
- Relation
- PLoS ONE Vol. 13, Issue 2, no. e0192192
- Publisher Link
- http://dx.doi.org/10.1371/journal.pone.0192192
- Publisher
- Public Library of Science
- Resource Type
- journal article
- Date
- 2018
- Description
- Wavelet Neural Networks are a combination of neural networks and wavelets and have been mostly used in the area of time-series prediction and control. Recently, Evolutionary Wavelet Neural Networks have been employed to develop cancer prediction models. The present study proposes to use ensembles of Evolutionary Wavelet Neural Networks. The search for a high quality ensemble is directed by a fitness function that incorporates the accuracy of the classifiers both independently and as part of the ensemble itself. The ensemble approach is tested on three publicly available biomedical benchmark datasets, one on Breast Cancer and two on Parkinson’s disease, using a 10-fold cross-validation strategy. Our experimental results show that, for the first dataset, the performance was similar to previous studies reported in literature. On the second dataset, the Evolutionary Wavelet Neural Network ensembles performed better than all previous methods. The third dataset is relatively new and this study is the first to report benchmark results.
- Subject
- neural networks; Parkinson disease; breast cancer; genetic algorithms; artificial neural networks; evolutionary algorithms; forecasting; optimization
- Identifier
- http://hdl.handle.net/1959.13/1384295
- Identifier
- uon:32038
- Identifier
- ISSN:1932-6203
- Rights
- © 2018 Khan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
- Language
- eng
- Full Text
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