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High Accuracy Classification of Populations with Breast Cancer: SVM Approach

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.

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

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

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

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

Copyright © 2023 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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