Machine Learning-Based Prediction of Breast Cancer
Maab A. Srour, Mohammed M. Dweib, Ibrahim Dweib · International Journal of Modeling and Optimization · 2025
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.