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

Sultan Almotairi (), Elsayed Badr (), Mustafa Abdul Salam and Hagar Ahmed
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Sultan Almotairi: Department of Computer Science, College of Computer and Information Sciences, Majmaah University, Al-Majmaah 11952, Saudi Arabia
Elsayed Badr: Scientific Computing Department, Faculty of Computers and Artificial Intelligence, Benha University, Benha 13511, Egypt
Mustafa Abdul Salam: Artificial Intelligence Department, Faculty of Computers and Artificial Intelligence, Benha University, Benha 13511, Egypt
Hagar Ahmed: Scientific Computing Department, Faculty of Computers and Artificial Intelligence, Benha University, Benha 13511, Egypt

Mathematics, 2023, vol. 11, issue 14, 1-25

Abstract: 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.

Keywords: support vector machine; Harris hawks optimization; scaling techniques; parallel processing (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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