期刊
ALEXANDRIA ENGINEERING JOURNAL
卷 61, 期 3, 页码 2520-2534出版社
ELSEVIER
DOI: 10.1016/j.aej.2021.07.024
关键词
Machine learning; Support vector machine; Grey Wolf optimizer; Scaling techniques; Breast cancer; Parallel processing
资金
- Deanship of Sci-entific Research at Majmaah University [RGP-2019-29]
The study presents a novel approach to improve breast cancer diagnosis accuracy, including enhancing support vector machine performance, introducing new scaling techniques, and utilizing parallel techniques to enhance efficiency.
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. (C) 2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University
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