High Accuracy Classification of Populations with Breast Cancer: SVM Approach

Philip de Melo; Mane Davtyan

doi:doi:10.11648/j.crj.20231103.13

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High Accuracy Classification of Populations with Breast Cancer: SVM Approach

Philip de Melo, Mane Davtyan

Published in Cancer Research Journal (Volume 11, Issue 3)

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), 2024. 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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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

Philip de Melo

Computer Science Department, Bowie State University, Bowie, USA
Mane Davtyan

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

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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

Copy | Download

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

Copy | Download

@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}
}

Copy | Download

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  -

Copy | Download