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Accurate classification of cancer data plays a vital role in early diagnosis, effective treatment planning, and patient survival prediction. This paper presents a comparative analysis of various machine learning classification algorithms applied to cancer datasets. The study evaluates algorithms such as Decision Tree, Support Vector Machine (SVM), Random Forest, K-Nearest Neighbor (KNN), and Naïve Bayes to determine their performance in identifying cancer types or malignancy levels. Data preprocessing techniques, including normalization and feature selection, are applied to improve model accuracy and reduce noise. The models are trained and tested using benchmark cancer datasets, such as the Wisconsin Breast Cancer Dataset (WBCD), to ensure reliability. Performance metrics such as accuracy, precision, recall, and F1-score are used to assess the effectiveness of each algorithm. Experimental results reveal that ensemble-based and kernel-based models achieve higher predictive performance compared to simple classifiers. This study demonstrates the importance of selecting an appropriate classification algorithm for cancer data analysis and supports the integration of machine learning in medical diagnosis systems.
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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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