Breast Cancer Diagnosis Using a Novel Parallel Support Vector Machine with Harris Hawks Optimization

Three contributions are proposed. Firstly, a novel hybrid classifier (HHO-SVM) is introduced, which is a combination between the Harris hawks optimization (HHO) and a support vector machine (SVM) is introduced. Second, the performance of the HHO-SVM is enhanced using the conventional normalization m...

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Bibliographic Details
Published in:Mathematics (Basel) 2023-07, Vol.11 (14), p.3251
Main Authors: Almotairi, Sultan, Badr, Elsayed, Abdul Salam, Mustafa, Ahmed, Hagar
Format: Article
Language:English
Subjects:
Accuracy
Artificial intelligence
Breast cancer
Cancer
Classification
Comparative analysis
Datasets
Decision trees
Diagnosis
Feature selection
Food science
Harris hawks optimization
Linear programming
Machine learning
Medical diagnosis
Optimization
Optimization algorithms
parallel processing
Regression analysis
Scaling
scaling techniques
support vector machine
Support vector machines
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Summary:Three contributions are proposed. Firstly, a novel hybrid classifier (HHO-SVM) is introduced, which is a combination between the Harris hawks optimization (HHO) and a support vector machine (SVM) is introduced. Second, the performance of the HHO-SVM is enhanced using the conventional normalization method. The final contribution is to improve the efficiency of the HHO-SVM by adopting a parallel approach that employs the data distribution. The proposed models are evaluated using the Wisconsin Diagnosis Breast Cancer (WDBC) dataset. The results show that the HHO-SVM achieves a 98.24% accuracy rate with the normalization scaling technique, outperforming other related works. On the other hand, the HHO-SVM achieves a 99.47% accuracy rate with the equilibration scaling technique, which is better than other previous works. Finally, to compare the three effective scaling strategies on four CPU cores, the parallel version of the proposed model provides an acceleration of 3.97.
ISSN:2227-7390
2227-7390
DOI:10.3390/math11143251