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Comparison of NEAT and Backpropagation Neural Network on Breast Cancer Diagnosis

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
Hamza Turabieh
10.5120/ijca2016909245

Hamza Turabieh. Article: Comparison of NEAT and Backpropagation Neural Network on Breast Cancer Diagnosis. International Journal of Computer Applications 139(8):40-44, April 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

@article{key:article,
	author = {Hamza Turabieh},
	title = {Article: Comparison of NEAT and Backpropagation Neural Network on Breast Cancer Diagnosis},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {139},
	number = {8},
	pages = {40-44},
	month = {April},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}
}

Abstract

In this paper we present a comparison between NeuroEvolution of Augmenting Typologies (NEAT) algorithm with Backpropagation Neural Network for the prediction of breast cancer. Machine learning algorithms could be used to enhance the performance of medical practitioners in the diagnosis of breast cancer. NEAT is a promising machine learning algorithm, which combines genetic algorithms and neural network. We compare the performance of these two algorithms on a standard benchmark dataset. Our results demonstrate that NEAT outperforms Backpropagation Neural Network, and we show that experimentally that NEAT has better generalization and much lower computational cost.

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Keywords

NEAT, Backpropagation, Breast Cancer.