The Impact of ADASYN on Distance-Based, Tree-Based, Ensemble, and Boosting Models in Breast Cancer Prediction

Investigates the impact of Adaptive Synthetic Sampling (ADASYN) on breast cancer classification across diverse ML models. Results show significant recall improvements for KNN (90.5% to 95.2%) and XGBoost (88.1% to 95.2%), demonstrating ADASYN's effectiveness in enhancing sensitivity to minority-class cases.