New Sequential and Parallel Support Vector Machine with Grey Wolf Optimizer for Breast Cancer Diagnosis

Breast cancer is one of the most common types of cancer worldwide. Early detection of cancer increases the probability of recovery. This work has three contributions. The first contribution is improving the performance of support vector machine (SVM) using a recent grey wolf optimizer (GWO) for diag...

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Autores principales:	Elsayed Badr, Sultan Almotairi, Mustafa Abdul Salam, Hagar Ahmed
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2022
Materias:	Machine learning Support vector machine Grey Wolf optimizer Scaling techniques Breast cancer Parallel processing Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/72818dbc2ee64dce88fd98b4395e2633
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spelling	oai:doaj.org-article:72818dbc2ee64dce88fd98b4395e26332021-12-02T04:59:41ZNew Sequential and Parallel Support Vector Machine with Grey Wolf Optimizer for Breast Cancer Diagnosis1110-016810.1016/j.aej.2021.07.024https://doaj.org/article/72818dbc2ee64dce88fd98b4395e26332022-03-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1110016821004890https://doaj.org/toc/1110-0168Breast cancer is one of the most common types of cancer worldwide. Early detection of cancer increases the probability of recovery. This work has three contributions. The first contribution is improving the performance of support vector machine (SVM) using a recent grey wolf optimizer (GWO) for diagnosis breast cancer with efficient scaling techniques. The second contribution is proposing three efficient scaling techniques against the classical normalization technique. The last contribution is using a parallel technique which applies task distribution to improve the efficiency of GWO. The proposed sequential model is applied on two different datasets, Wisconsin diagnosis breast cancer (WDBC) dataset and Electronic Health Records (EHR). Experimental results of WDBC show that the proposed hybrid GWO-SVM model achieves 98.60% with normalization scaling. Also, using the proposed scaling techniques with the proposed GWO-SVM model gives a fast convergence and achieves accuracy rate by 99.30%. The parallel version of the proposed model achieves a speedup by 3.9 on four CPU cores. On the other hand, Experimental results of EHR show that the proposed hybrid GWO-SVM model achieves 93.26% with normalization scaling against 82.05 for SVM.Elsayed BadrSultan AlmotairiMustafa Abdul SalamHagar AhmedElsevierarticleMachine learningSupport vector machineGrey Wolf optimizerScaling techniquesBreast cancerParallel processingEngineering (General). Civil engineering (General)TA1-2040ENAlexandria Engineering Journal, Vol 61, Iss 3, Pp 2520-2534 (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	Machine learning Support vector machine Grey Wolf optimizer Scaling techniques Breast cancer Parallel processing Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Machine learning Support vector machine Grey Wolf optimizer Scaling techniques Breast cancer Parallel processing Engineering (General). Civil engineering (General) TA1-2040 Elsayed Badr Sultan Almotairi Mustafa Abdul Salam Hagar Ahmed New Sequential and Parallel Support Vector Machine with Grey Wolf Optimizer for Breast Cancer Diagnosis
description	Breast cancer is one of the most common types of cancer worldwide. Early detection of cancer increases the probability of recovery. This work has three contributions. The first contribution is improving the performance of support vector machine (SVM) using a recent grey wolf optimizer (GWO) for diagnosis breast cancer with efficient scaling techniques. The second contribution is proposing three efficient scaling techniques against the classical normalization technique. The last contribution is using a parallel technique which applies task distribution to improve the efficiency of GWO. The proposed sequential model is applied on two different datasets, Wisconsin diagnosis breast cancer (WDBC) dataset and Electronic Health Records (EHR). Experimental results of WDBC show that the proposed hybrid GWO-SVM model achieves 98.60% with normalization scaling. Also, using the proposed scaling techniques with the proposed GWO-SVM model gives a fast convergence and achieves accuracy rate by 99.30%. The parallel version of the proposed model achieves a speedup by 3.9 on four CPU cores. On the other hand, Experimental results of EHR show that the proposed hybrid GWO-SVM model achieves 93.26% with normalization scaling against 82.05 for SVM.
format	article
author	Elsayed Badr Sultan Almotairi Mustafa Abdul Salam Hagar Ahmed
author_facet	Elsayed Badr Sultan Almotairi Mustafa Abdul Salam Hagar Ahmed
author_sort	Elsayed Badr
title	New Sequential and Parallel Support Vector Machine with Grey Wolf Optimizer for Breast Cancer Diagnosis
title_short	New Sequential and Parallel Support Vector Machine with Grey Wolf Optimizer for Breast Cancer Diagnosis
title_full	New Sequential and Parallel Support Vector Machine with Grey Wolf Optimizer for Breast Cancer Diagnosis
title_fullStr	New Sequential and Parallel Support Vector Machine with Grey Wolf Optimizer for Breast Cancer Diagnosis
title_full_unstemmed	New Sequential and Parallel Support Vector Machine with Grey Wolf Optimizer for Breast Cancer Diagnosis
title_sort	new sequential and parallel support vector machine with grey wolf optimizer for breast cancer diagnosis
publisher	Elsevier
publishDate	2022
url	https://doaj.org/article/72818dbc2ee64dce88fd98b4395e2633
work_keys_str_mv	AT elsayedbadr newsequentialandparallelsupportvectormachinewithgreywolfoptimizerforbreastcancerdiagnosis AT sultanalmotairi newsequentialandparallelsupportvectormachinewithgreywolfoptimizerforbreastcancerdiagnosis AT mustafaabdulsalam newsequentialandparallelsupportvectormachinewithgreywolfoptimizerforbreastcancerdiagnosis AT hagarahmed newsequentialandparallelsupportvectormachinewithgreywolfoptimizerforbreastcancerdiagnosis
_version_	1718400894668636160

New Sequential and Parallel Support Vector Machine with Grey Wolf Optimizer for Breast Cancer Diagnosis

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