doi: 10.7763/IJMO.2025.V15.866
Machine Learning-Based Prediction of Breast Cancer
Maab A. Srour
1,*
,
Mohammed M. Dweib
2
,
Ibrahim M. Dweib
2
- 1 CIS, Technology and Applied Science, Al Quds Open University, Bethlehem, Palestine
- 2 Math and IT, Center for Preparatory Studies, Sultan Qaboos University, Muscat, Oman
- Manuscript received July 11, 2024
- revised December 21, 2024
- accepted January 19, 2025
- published March 27, 2025
Abstract
Breast cancer stands as a significant global health challenge, affecting millions of women annually. The urgency of early detection as a pivotal factor in mitigating its impact has prompted the exploration of advanced diagnostic tools, particularly Computer-Aided Detection and Diagnosis (CAD) technologies. This study capitalizes on recent developments in CAD systems and associated methodologies to enhance the early detection of breast cancer. Utilizing the Wisconsin Breast Cancer Diagnostic (WBCD) dataset, this research conducts a thorough analysis of multiple machine learning models. Despite the dataset's modest size, it offers valuable insights. The information undergoes careful examination and is applied to various machine learning models, including random forest, logistic regression, decision tree, and K-nearest neighbor, for predictive analysis. Upon comparative evaluation, the logistic regression model emerges as the most promising, achieving an accuracy of 98%. The outcomes of this research contribute valuable insights to the refinement of breast cancer prediction models, promising advancements in timely and effective interventions.
Keywords
- breast cancer prediction machine learning
- Wisconsin Breast Cancer Diagnostic (WBCD)
- Artificial Intelligence (AI)
- K-Nearest Neighbors (KNN)
How to Cite
Maab A. Srour, Mohammed M. Dweib, and Ibrahim M. Dweib, "Machine Learning-Based Prediction of Breast Cancer," International Journal of Modeling and Optimization, vol. 15, no. 1, pp. 9-16, 2025. https://doi.org/ 10.7763/IJMO.2025.V15.866
Copyright & License
Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).