1 Department of Electrical Engineering, Centrale Nantes University, France.
2 Department of Technology, Njala University, Sierra Leone.
3 Department of Electrical and Computer Engineering, University of Delaware, USA.
World Journal of Advanced Research and Reviews, 2025, 25(01), 1357-1360
Article DOI: 10.30574/wjarr.2025.25.1.0179
Received on 01 December 2024; revised on 13 January 2025; accepted on 15 January 2025
Breast cancer remains a significant global health challenge, where accurate prediction plays a vital role in early diagnosis and effective treatment, ultimately saving lives. This study evaluates the performance of three machine learning models; Support Vector Machine (SVM), Random Forest Classifier, and XGBoost for breast cancer prediction. Using the Wisconsin Breast Cancer Dataset, the models were assessed based on their accuracy. The experimental results demonstrated that SVM outperformed the other models, while both XGBoost and the Random Forest Classifier achieved just slightly lower accuracies. This research underscores the potential of machine learning models in enhancing breast cancer prediction and highlights their importance in advancing early detection and treatment strategies.
Support Vector Machines; Random Forest Classifier; XGBoost; Machine Learning; Breast Cancer
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Anthony Sowah, Titus Santigie-Sankoh, Vero Bai-Anku and Eric Jhessim. Insights into breast cancer: A simple machine learning method for early disease detection. World Journal of Advanced Research and Reviews, 2025, 25(01), 1357-1360. Article DOI: https://doi.org/10.30574/wjarr.2025.25.1.0179.
Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0