Research Article
Breast Cancer Diagnosis based on Genetic Algorithms and Neural Networks
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 180 - Issue 26 |
| Published: Mar 2018 |
| Authors: Khaled M. A. Alalayah, Siham A. M. Almasani, Wadeea A. A. Qaid, Ibrahim Abdulrab Ahmed |
10.5120/ijca2018916605
|
Khaled M. A. Alalayah, Siham A. M. Almasani, Wadeea A. A. Qaid, Ibrahim Abdulrab Ahmed . Breast Cancer Diagnosis based on Genetic Algorithms and Neural Networks. International Journal of Computer Applications. 180, 26 (Mar 2018), 42-44. DOI=10.5120/ijca2018916605
@article{ 10.5120/ijca2018916605,
author = { Khaled M. A. Alalayah,Siham A. M. Almasani,Wadeea A. A. Qaid,Ibrahim Abdulrab Ahmed },
title = { Breast Cancer Diagnosis based on Genetic Algorithms and Neural Networks },
journal = { International Journal of Computer Applications },
year = { 2018 },
volume = { 180 },
number = { 26 },
pages = { 42-44 },
doi = { 10.5120/ijca2018916605 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2018
%A Khaled M. A. Alalayah
%A Siham A. M. Almasani
%A Wadeea A. A. Qaid
%A Ibrahim Abdulrab Ahmed
%T Breast Cancer Diagnosis based on Genetic Algorithms and Neural Networks%T
%J International Journal of Computer Applications
%V 180
%N 26
%P 42-44
%R 10.5120/ijca2018916605
%I Foundation of Computer Science (FCS), NY, USA
Abstract
This paper proposes to use of hybrid genetic algorithm with artificial neural networks technique for diagnosing the Breast Cancer in order to give more accuracy than using neural network, since the performance of hybrid genetic algorithm with artificial neural networks for diagnosis of the Breast Cancer can measure by the value of fitness function. The number of hidden layers in the neural network has a significant effect on the classification performance and the best diagnoses performance average is attained when the number of layers equal three. The result by the using of GANN technique up to 94%.
References
- Ahmed Khalid, Noureldien A. Noureldien. Determining the Efficient Structure of Feedforward Neural Network to Classify Breast Cancer Dataset. (IJACSA) International Journal of Advanced Computer Science and Applications, 2013.Vol. XXX, No. XXX,pp:1-4.
- Cook D, Ragsdale C, Major R. Combining a neural network with a genetic algorithm for process parameter optimization. Eng Appl Artif Intell. 2000.Vol.13(4):pp.391-6.
- Hirose Y, Yamashita K, Hijiya S. Back-propagation algorithm which varies the number of hidden units. Neural Networks. 1991. Vol.4(1):pp.61-6.
- Janghel, R.R.; Shukla, A.; Tiwari, R.; Kala, R. (2010). “Breast Cancer Diagnostic Technique using Symbiotic Adaptive Neuro-Evolution (SANE)”. ProceedingsInternational conference of Soft Computing and Pattern Recognition (SoCPaR-2010), 7th-10th Dec, ABV-IIITM, Gwalior. pp: 326-329.
- Jemal, A., Siegel, R., Ward, E., Murray, T., Xu, J., Thun, M.J. Cancer Statistics, 2007. CA Cancer J Clin, Vol. 57, 2007. pp: 43-66.
- [K. V. Price, R. M. Storn, and J. A. Lampinen, Differential Evolution—A Practical Approach to Global Optimization. Berlin, Germany: SpringerVerlag, 2005.
- M. McInerney, and A.P. Dhawan., Use of Genetic Algorithms with Backpropagation in Training of Feedforward Neural Networks, Proceeding of IEEE International Conference on Neural Network, 1993, Vol 1, No 3, 28 March-1 April, pg 203-208.
- Netnevitsky, M., Artificial Intelligence: A Guide To Intelligent Techniques, Addison Wesley, Harlow, 2005,2nd ed.
- S.J. Marshall., & R.F. Harrison, Optimization and Training on Feed-forward Neural Networks by GeneticAlgorithms, IEEE 2nd International Conference on Artificial Neural Network, 1991, pg 39-43.
- UCI Machine Learning Repository. [Online]. Available: https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Original)
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