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

Author

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  • Sultan Almotairi

    (Department of Computer Science, College of Computer and Information Sciences, Majmaah University, Al-Majmaah 11952, Saudi Arabia
    Department of Computer Science, Faculty of Computer and Information Systems, Islamic University of Madinah, Medinah 42351, Saudi Arabia)

  • Elsayed Badr

    (Scientific Computing Department, Faculty of Computers and Artificial Intelligence, Benha University, Benha 13511, Egypt
    Computer Science Department, Integrated Thebes Institutes, Cairo 11331, Egypt)

  • Mustafa Abdul Salam

    (Artificial Intelligence Department, Faculty of Computers and Artificial Intelligence, Benha University, Benha 13511, Egypt
    Faculty of Computer Studies, Arab Open University, Cairo 11211, Egypt)

  • Hagar Ahmed

    (Scientific Computing Department, Faculty of Computers and Artificial Intelligence, Benha University, Benha 13511, Egypt)

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.

Suggested Citation

  • Sultan Almotairi & Elsayed Badr & Mustafa Abdul Salam & Hagar Ahmed, 2023. "Breast Cancer Diagnosis Using a Novel Parallel Support Vector Machine with Harris Hawks Optimization," Mathematics, MDPI, vol. 11(14), pages 1-25, July.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:14:p:3251-:d:1201471
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    References listed on IDEAS

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    1. Elsayed Badr & Mustafa Abdul Salam & Sultan Almotairi & Hagar Ahmed & Roberto Natella, 2021. "From Linear Programming Approach to Metaheuristic Approach: Scaling Techniques," Complexity, Hindawi, vol. 2021, pages 1-10, February.
    2. Joseph Elble & Nikolaos Sahinidis, 2012. "Scaling linear optimization problems prior to application of the simplex method," Computational Optimization and Applications, Springer, vol. 52(2), pages 345-371, June.
    3. Saba Bashir & Usman Qamar & Farhan Khan, 2015. "Heterogeneous classifiers fusion for dynamic breast cancer diagnosis using weighted vote based ensemble," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(5), pages 2061-2076, September.
    4. Charlotte Truchet & Alejandro Arbelaez & Florian Richoux & Philippe Codognet, 2016. "Estimating parallel runtimes for randomized algorithms in constraint solving," Journal of Heuristics, Springer, vol. 22(4), pages 613-648, August.
    5. Na Liu & Jiang Shen & Man Xu & Dan Gan & Er-Shi Qi & Bo Gao, 2018. "Improved Cost-Sensitive Support Vector Machine Classifier for Breast Cancer Diagnosis," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-13, November.
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    Cited by:

    1. Tao Yu & Wei Huang & Xin Tang, 2023. "A Novel Fuzzy Unsupervised Quadratic Surface Support Vector Machine Based on DC Programming: An Application to Credit Risk Management," Mathematics, MDPI, vol. 11(22), pages 1-14, November.

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