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Breast Cancer Diagnosis based on Genetic Algorithms and Neural Networks

by Khaled M. A. Alalayah, Siham A. M. Almasani, Wadeea A. A. Qaid, Ibrahim Abdulrab Ahmed
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 180 - Number 26
Year of Publication: 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 },
issue_date = { Mar 2018 },
volume = { 180 },
number = { 26 },
month = { Mar },
year = { 2018 },
issn = { 0975-8887 },
pages = { 42-44 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume180/number26/29124-2018916605/ },
doi = { 10.5120/ijca2018916605 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:01:53.413902+05:30
%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
%J International Journal of Computer Applications
%@ 0975-8887
%V 180
%N 26
%P 42-44
%D 2018
%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
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  10. UCI Machine Learning Repository. [Online]. Available: https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Original)
Index Terms

Computer Science
Information Sciences

Keywords

Classification Neural network Genetic Algorithms Breast Cancer Performance.

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