Ensemble Learning Approach Reveals Significant Clinical Attributes from Real-World Breast Cancer Cases - Angga Aditya Permana; Muhammad Fahrury Romdendine (Universitas Islam Raden Rahmat, Malang, 2024)
Breast cancer has become on of the leading causes of death in Indonesia. This study contributes to global efforts to combat breast cancer by improving patient outcome prediction accuracy. This study employed ensemble learning techniques such as Random Forest, XGBoost, and LightGBM. The results of the study demonstrates LightGBM's superior performance (accuracy=85%, ROC-AUC=81%, AUPR=85%). Notably, all three algorithms identify key clinical attributes: