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Breast cancer remains one of the most common and life-threatening diseases among women worldwide. Early detection and accurate prognosis play a vital role in improving patient survival rates. This paper focuses on the diagnosis and prognosis analysis of breast cancer using machine learning and data mining classification techniques. The proposed approach employs algorithms such as Decision Trees, Support Vector Machines (SVM), Random Forest, and Naïve Bayes to classify tumor types as benign or malignant based on clinical and histopathological data. Data preprocessing, feature selection, and normalization are performed to enhance model accuracy and reduce computational complexity. The models are trained and validated using benchmark datasets such as the Wisconsin Breast Cancer Dataset (WBCD). Comparative analysis demonstrates that ensemble and hybrid models achieve higher prediction accuracy and robustness than traditional methods. The results highlight the potential of machine learning-based classification in assisting medical professionals with early diagnosis, treatment planning, and personalized healthcare.
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Copyright © 2020, This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC-BY-NY-SA). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Corresponding Author: T. Chalapathi Rao, chalapathi520@gmail.com
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Conflict of interest: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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