New Sequential and Parallel Support Vector Machine with Grey Wolf Optimizer for Breast Cancer Diagnosis
| Autor: | E. M. Badr, Sultan Almotairi, Mustafa Abdul Salam, Hagar Ahmed |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Normalization (statistics)
Multi-core processor Speedup Support vector machine Computer science General Engineering medicine.disease Engineering (General). Civil engineering (General) Breast cancer ComputingMethodologies_PATTERNRECOGNITION Scaling techniques Grey Wolf optimizer Convergence (routing) Machine learning medicine Parallel processing Sequential model TA1-2040 Algorithm Scaling |
| Zdroj: | Alexandria Engineering Journal, Vol 61, Iss 3, Pp 2520-2534 (2022) |
| ISSN: | 1110-0168 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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