Breast Cancer Classification Using Ensemble Voting: A Feature Selection Approach

Breast cancer is one of the most common and deadly diseases affecting women around the worldwide. It is specially affecting in regions where has limited access to advanced diagnostic tools. Recent studies have shown that blood-based biomarkers can give a cost-effective alternative for early detection. This paper represents a machine learning-based approach for classifying breast cancer using clinical and biomedicial data. We have used the Breast Cancer Coimbra dataset for our study. We employed four filter-based feature selection methods—Mutual Information, Chi-Square, ANOVA F-test, and Pearson Correlation Coefficient—to identify the most relevant features for classification. We have applied two classifiers (AdaBoost and Ensemble Voting Classifier) to enhance predictive accuracy. The ensemble model achieved an accuracy of 82.86%. Key features such as glucose, HOMA, insulin, resistin, and age consistently contributed across all selected methods.It highlights that a few of the features has a great significance in breast cancer prediction. This study also try to investigate the reasons behind the missclassification cases. Our results show that using statistical feature selection with ensemble learning reasonable helps to boost the accuracy of breast cancer prediction. This approach helps the model focus on the most important features.