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Evolutionary Wavelet Neural Network ensembles for breast cancer and Parkinson’s disease prediction

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  • Maryam Mahsal Khan
  • Alexandre Mendes
  • Stephan K Chalup

Abstract

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.

Suggested Citation

  • Maryam Mahsal Khan & Alexandre Mendes & Stephan K Chalup, 2018. "Evolutionary Wavelet Neural Network ensembles for breast cancer and Parkinson’s disease prediction," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-15, February.
  • Handle: RePEc:plo:pone00:0192192
    DOI: 10.1371/journal.pone.0192192
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    References listed on IDEAS

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    1. Mohammad Nazmul Haque & Nasimul Noman & Regina Berretta & Pablo Moscato, 2016. "Heterogeneous Ensemble Combination Search Using Genetic Algorithm for Class Imbalanced Data Classification," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-28, January.
    2. Min-Wei Huang & Chih-Wen Chen & Wei-Chao Lin & Shih-Wen Ke & Chih-Fong Tsai, 2017. "SVM and SVM Ensembles in Breast Cancer Prediction," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-14, January.
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