| Peer-Reviewed

High Accuracy Classification of Populations with Breast Cancer: SVM Approach

Received: 1 July 2023     Accepted: 27 July 2023     Published: 15 August 2023
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Abstract

Breast cancer is one of the most common cancers diagnosed in the United States. Breast cancer can occur in both men and women. The number of deaths associated with this disease is steadily declining, largely due to factors such as earlier detection and a new personalized approach to treatment. In this article, we offer a highly accurate and reliable classification approach based on feature engineering and an improved support vector machine (SVM) classifier. We examine a dataset with 30 features and use in-depth data analytics and visualization to pinpoint the top nine features that have a significant impact on classification accuracy. The SVM classification outperformed other classifiers, including kernel extensions, with a high accuracy of 99.12%. The study stresses the value of machine learning in medical diagnosis, notably in the early detection of breast cancer, and indicates the possibility for further research in this area utilizing deep learning architectures. Early detection of breast cancer is critical, and our findings contribute to the growing body of knowledge in this area, opening new avenues for improving cancer diagnosis and patient care.

Published in Cancer Research Journal (Volume 11, Issue 3)
DOI 10.11648/j.crj.20231103.13
Page(s) 94-104
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2023. Published by Science Publishing Group

Keywords

Breast Cancer, Support Vector Machine, Feature Engineering, Early Detection, Machine Learning, Classification, Data Analytics

References
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Cite This Article
  • APA Style

    Philip de Melo, Mane Davtyan. (2023). High Accuracy Classification of Populations with Breast Cancer: SVM Approach. Cancer Research Journal, 11(3), 94-104. https://doi.org/10.11648/j.crj.20231103.13

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    ACS Style

    Philip de Melo; Mane Davtyan. High Accuracy Classification of Populations with Breast Cancer: SVM Approach. Cancer Res. J. 2023, 11(3), 94-104. doi: 10.11648/j.crj.20231103.13

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    AMA Style

    Philip de Melo, Mane Davtyan. High Accuracy Classification of Populations with Breast Cancer: SVM Approach. Cancer Res J. 2023;11(3):94-104. doi: 10.11648/j.crj.20231103.13

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  • @article{10.11648/j.crj.20231103.13,
      author = {Philip de Melo and Mane Davtyan},
      title = {High Accuracy Classification of Populations with Breast Cancer: SVM Approach},
      journal = {Cancer Research Journal},
      volume = {11},
      number = {3},
      pages = {94-104},
      doi = {10.11648/j.crj.20231103.13},
      url = {https://doi.org/10.11648/j.crj.20231103.13},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.crj.20231103.13},
      abstract = {Breast cancer is one of the most common cancers diagnosed in the United States. Breast cancer can occur in both men and women. The number of deaths associated with this disease is steadily declining, largely due to factors such as earlier detection and a new personalized approach to treatment. In this article, we offer a highly accurate and reliable classification approach based on feature engineering and an improved support vector machine (SVM) classifier. We examine a dataset with 30 features and use in-depth data analytics and visualization to pinpoint the top nine features that have a significant impact on classification accuracy. The SVM classification outperformed other classifiers, including kernel extensions, with a high accuracy of 99.12%. The study stresses the value of machine learning in medical diagnosis, notably in the early detection of breast cancer, and indicates the possibility for further research in this area utilizing deep learning architectures. Early detection of breast cancer is critical, and our findings contribute to the growing body of knowledge in this area, opening new avenues for improving cancer diagnosis and patient care.},
     year = {2023}
    }
    

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  • TY  - JOUR
    T1  - High Accuracy Classification of Populations with Breast Cancer: SVM Approach
    AU  - Philip de Melo
    AU  - Mane Davtyan
    Y1  - 2023/08/15
    PY  - 2023
    N1  - https://doi.org/10.11648/j.crj.20231103.13
    DO  - 10.11648/j.crj.20231103.13
    T2  - Cancer Research Journal
    JF  - Cancer Research Journal
    JO  - Cancer Research Journal
    SP  - 94
    EP  - 104
    PB  - Science Publishing Group
    SN  - 2330-8214
    UR  - https://doi.org/10.11648/j.crj.20231103.13
    AB  - Breast cancer is one of the most common cancers diagnosed in the United States. Breast cancer can occur in both men and women. The number of deaths associated with this disease is steadily declining, largely due to factors such as earlier detection and a new personalized approach to treatment. In this article, we offer a highly accurate and reliable classification approach based on feature engineering and an improved support vector machine (SVM) classifier. We examine a dataset with 30 features and use in-depth data analytics and visualization to pinpoint the top nine features that have a significant impact on classification accuracy. The SVM classification outperformed other classifiers, including kernel extensions, with a high accuracy of 99.12%. The study stresses the value of machine learning in medical diagnosis, notably in the early detection of breast cancer, and indicates the possibility for further research in this area utilizing deep learning architectures. Early detection of breast cancer is critical, and our findings contribute to the growing body of knowledge in this area, opening new avenues for improving cancer diagnosis and patient care.
    VL  - 11
    IS  - 3
    ER  - 

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Author Information
  • Computer Science Department, Bowie State University, Bowie, USA

  • College of Science and Engineering, American University of Armenia, Yerevan, Armenia

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