Comparative Study of Feature Selection Techniques for Breast Cancer Prediction

Abstract

This study presents a comparative analysis of five feature selection methods—Chi-Square, Mutual Information, RFE, LASSO, and Random Forest Importance—applied to the Breast Cancer Wisconsin Diagnostic dataset. Their effectiveness was evaluated using Logistic Regression, SVM, and Random Forest classifiers based on accuracy, F1-score, ROC-AUC, runtime, and Jaccard-based stability. RFE achieved the highest predictive performance, whereas Chi-Square and Mutual Information provided the strongest stability and fastest computation. Random Forest Importance offered a balanced trade-off, while LASSO showed reduced stability due to aggressive regularization. The results highlight clear performance–stability trade-offs and provide practical guidelines for selecting reliable feature selection techniques in breast cancer prediction.