Ensemble Machine Learning for Enhanced Breast Cancer Prediction: A Comparative Study

Breast cancer poses a significant threat to women's health, affecting one in every eight women globally and often leading to fatal outcomes due to delayed detection in advanced stages. Recent advancements in machine learning have opened doors to early detection possibilities. This study explores various machine learning algorithms, including K- Nearest Neighbor (KNN), Support Vector Machine (SVM), Multi- Layer Perceptron (MLP), Decision Tree (DT), Logistic Regression (LR), Naive Bayes (NB), Random Forest (RF), Ada Boost (AB), Gradient Boosting (GB), and XGboost (XGB). The employed algorithms, along with nested ensembles of Bagging, Boosting, Stacking, and Voting, predicted whether a cell is benign or malignant using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. Utilizing the Chi-square feature selection technique, this study identified 21 essential features to enhance prediction accuracy. Results of this study indicate that MLP LR achieved the highest accuracy of 98.25%, closely followed by SVM with 97.08% accuracy. Notably, the Voting classifier yielded the highest accuracy of 99.42% among the ensemble methods. These findings suggest that the research model holds promise for accurate breast cancer prediction, thus contributing to increased awareness and early intervention.