Breast Cancer Detection Using Machine Learning Algorithms

Developing a breast cancer detection solution using Python, Flask, HTML, CSS, and machine learning. Employing pandas and NumPy for data manipulation, while utilizing matplotlib and seaborn for insightful visualization to enhance model training and diagnostic accuracy. The system offers a user-friendly web interface allowing users to input clinical data for analysis. Through sophisticated data preprocessing and feature extraction methods, combined with powerful machine learning algorithms, including logistic regression and support vector machines, the system provides accurate predictions regarding the presence of breast cancer. The integration of Matplotlib and Seaborn enables the generation of insightful visualizations, enhancing the interpretability of the model predictions. Future iterations may focus on refining the model's performance and incorporating additional features to further enhance its clinical utility.

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Publication Details

Journal
International Research Journal on Advanced Engineering and Management (IRJAEM)
Published
2025-03-28
DOI
https://doi.org/10.47392/irjaem.2025.0132
Citations
1
Primary Topic
AI in cancer detection
Type
article
Field-Weighted Citation Impact
0.00
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article

Breast Cancer Detection Using Machine Learning Algorithms

Anup M. Vanage, Anupreeta More, S. P. Deshpande, M Fatma et al.
1 citations
International Research Journal on Advanced Engineering and Management (IRJAEM)
AI in cancer detection
article

Breast Cancer Detection Using Machine Learning Algorithms

Anup M. Vanage, Anupreeta More, S. P. Deshpande, M Fatma, Zaara Naikwadi
article en
1 citations

Abstract

Developing a breast cancer detection solution using Python, Flask, HTML, CSS, and machine learning. Employing pandas and NumPy for data manipulation, while utilizing matplotlib and seaborn for insightful visualization to enhance model training and diagnostic accuracy. The system offers a user-friendly web interface allowing users to input clinical data for analysis. Through sophisticated data preprocessing and feature extraction methods, combined with powerful machine learning algorithms, including logistic regression and support vector machines, the system provides accurate predictions regarding the presence of breast cancer. The integration of Matplotlib and Seaborn enables the generation of insightful visualizations, enhancing the interpretability of the model predictions. Future iterations may focus on refining the model's performance and incorporating additional features to further enhance its clinical utility.

International Research Journal on Advanced Engineering and Management (IRJAEM)Vol. 3(03)
Bharat Serums and Vaccines (India) (IN), Naval War College (US)
Openalex Percentile: Top 96%
AI in cancer detection
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Breast Cancer Detection Using Machine Learning Algorithms — Anup M. Vanage, Anupreeta More, et al. · International Research Journal on Advanced Engineering and Management (IRJAEM) (2025) | TGRS Research Map | TGRS