Backpropagation neural network based on local search strategy and enhanced multi-objective evolutionary algorithm for breast cancer diagnosis

The role of intelligence techniques is becoming more signiﬁcant in detecting and diagnosis of medical data. However, the performance of such methods is based on the algorithms or technique. In this paper, we develop an intelligent technique using multiobjective evolutionary method hybrid with a loca...

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Main Authors:	Ashraf Osman, Ibrahim, Siti Mariyam, Shamsuddin, Abdulrazak, Yahya Saleh, Ahmed, Ali, Mohd Arfian, Ismail, Shahreen, Kasim
Format:	Article
Language:	English
Published:	Insight Society 2019
Subjects:	QA Mathematics QA75 Electronic computers. Computer science QA76 Computer software TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/25082/ http://umpir.ump.edu.my/id/eprint/25082/ http://umpir.ump.edu.my/id/eprint/25082/ http://umpir.ump.edu.my/id/eprint/25082/1/Backpropagation%20neural%20network%20based%20on%20local%20search%20strategy.pdf

id	ump-25082
recordtype	eprints
spelling	ump-250822019-10-24T07:25:53Z http://umpir.ump.edu.my/id/eprint/25082/ Backpropagation neural network based on local search strategy and enhanced multi-objective evolutionary algorithm for breast cancer diagnosis Ashraf Osman, Ibrahim Siti Mariyam, Shamsuddin Abdulrazak, Yahya Saleh Ahmed, Ali Mohd Arfian, Ismail Shahreen, Kasim QA Mathematics QA75 Electronic computers. Computer science QA76 Computer software TK Electrical engineering. Electronics Nuclear engineering The role of intelligence techniques is becoming more signiﬁcant in detecting and diagnosis of medical data. However, the performance of such methods is based on the algorithms or technique. In this paper, we develop an intelligent technique using multiobjective evolutionary method hybrid with a local search approach to enhance the backpropagation neural network. First, we enhance the famous multiobjective evolutionary algorithms, which is a non-dominated sorting genetic algorithm (NSGA-II). Then, we hybrid the enhanced algorithm with the local search strategy to ensures the acceleration of the convergence speed to the non-dominated front. In addition, such hybridization get the solutions achieved are well spread over it. As a result of using a local search method the quality of the Pareto optimal solutions are increased and all individuals in the population are enhanced. The key notion of the proposed algorithm was to show a new technique to settle automaticly artificial neural network design problem. The empirical results generated by the proposed intelligent technique evaluated by applying to the breast cancer dataset and emphasize the capability of the proposed algorithm to improve the results. The network size and accuracy results of the proposed method are better than the previous methods. Therefore, the method is then capable of finding a proper number of hidden neurons and error rates of the BP algorithm. Insight Society 2019 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/25082/1/Backpropagation%20neural%20network%20based%20on%20local%20search%20strategy.pdf Ashraf Osman, Ibrahim and Siti Mariyam, Shamsuddin and Abdulrazak, Yahya Saleh and Ahmed, Ali and Mohd Arfian, Ismail and Shahreen, Kasim (2019) Backpropagation neural network based on local search strategy and enhanced multi-objective evolutionary algorithm for breast cancer diagnosis. International Journal on Advanced Science, Engineering and Information Technology, 9 (2). pp. 609-615. ISSN 2088-5334 https://doi.org/10.18517/ijaseit.9.2.4986 https://doi.org/10.18517/ijaseit.9.2.4986
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA Mathematics QA75 Electronic computers. Computer science QA76 Computer software TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	QA Mathematics QA75 Electronic computers. Computer science QA76 Computer software TK Electrical engineering. Electronics Nuclear engineering Ashraf Osman, Ibrahim Siti Mariyam, Shamsuddin Abdulrazak, Yahya Saleh Ahmed, Ali Mohd Arfian, Ismail Shahreen, Kasim Backpropagation neural network based on local search strategy and enhanced multi-objective evolutionary algorithm for breast cancer diagnosis
description	The role of intelligence techniques is becoming more signiﬁcant in detecting and diagnosis of medical data. However, the performance of such methods is based on the algorithms or technique. In this paper, we develop an intelligent technique using multiobjective evolutionary method hybrid with a local search approach to enhance the backpropagation neural network. First, we enhance the famous multiobjective evolutionary algorithms, which is a non-dominated sorting genetic algorithm (NSGA-II). Then, we hybrid the enhanced algorithm with the local search strategy to ensures the acceleration of the convergence speed to the non-dominated front. In addition, such hybridization get the solutions achieved are well spread over it. As a result of using a local search method the quality of the Pareto optimal solutions are increased and all individuals in the population are enhanced. The key notion of the proposed algorithm was to show a new technique to settle automaticly artificial neural network design problem. The empirical results generated by the proposed intelligent technique evaluated by applying to the breast cancer dataset and emphasize the capability of the proposed algorithm to improve the results. The network size and accuracy results of the proposed method are better than the previous methods. Therefore, the method is then capable of finding a proper number of hidden neurons and error rates of the BP algorithm.
format	Article
author	Ashraf Osman, Ibrahim Siti Mariyam, Shamsuddin Abdulrazak, Yahya Saleh Ahmed, Ali Mohd Arfian, Ismail Shahreen, Kasim
author_facet	Ashraf Osman, Ibrahim Siti Mariyam, Shamsuddin Abdulrazak, Yahya Saleh Ahmed, Ali Mohd Arfian, Ismail Shahreen, Kasim
author_sort	Ashraf Osman, Ibrahim
title	Backpropagation neural network based on local search strategy and enhanced multi-objective evolutionary algorithm for breast cancer diagnosis
title_short	Backpropagation neural network based on local search strategy and enhanced multi-objective evolutionary algorithm for breast cancer diagnosis
title_full	Backpropagation neural network based on local search strategy and enhanced multi-objective evolutionary algorithm for breast cancer diagnosis
title_fullStr	Backpropagation neural network based on local search strategy and enhanced multi-objective evolutionary algorithm for breast cancer diagnosis
title_full_unstemmed	Backpropagation neural network based on local search strategy and enhanced multi-objective evolutionary algorithm for breast cancer diagnosis
title_sort	backpropagation neural network based on local search strategy and enhanced multi-objective evolutionary algorithm for breast cancer diagnosis
publisher	Insight Society
publishDate	2019
url	http://umpir.ump.edu.my/id/eprint/25082/ http://umpir.ump.edu.my/id/eprint/25082/ http://umpir.ump.edu.my/id/eprint/25082/ http://umpir.ump.edu.my/id/eprint/25082/1/Backpropagation%20neural%20network%20based%20on%20local%20search%20strategy.pdf
first_indexed	2023-09-18T22:38:20Z
last_indexed	2023-09-18T22:38:20Z
_version_	1777416745912369152

Backpropagation neural network based on local search strategy and enhanced multi-objective evolutionary algorithm for breast cancer diagnosis

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