A Hybrid Fuzzy–Ensemble Machine Learning Framework for Non-Invasive Prediction of HER2 Status in Breast Cancer

HER2 status determination is a crucial task in breast cancer prognosis and treatment,1 yet traditional diagnostic methods such as immunohistochemistry (IHC) and fluorescence in situ2 hybridization (FISH) are invasive, time-consuming, and costly. Motivated by the need for scalable3 and data-driven predictive approaches, we propose a hybrid machine learning framework that4 integrates ensemble learning with fuzzy modeling for HER2 prediction using routinely available5 clinical and immunohistochemical data. A dataset comprising 624 breast cancer patients from6 Mahdieh Clinic (Kermanshah, Iran) was analyzed, with extensive feature engineering, scaling, and7 class balancing applied. We developed an ensemble framework based on tree-based learners (Random8 Forest, XGBoost, and LightGBM), combined through ensemble strategies and enhanced using fuzzy9 feature representations and decision threshold optimization. The proposed hybrid model achieved10 an accuracy of 0.816, an F1-score of 0.814, and an area under the ROC curve (AUC) of 0.862 on11 the held-out test set, demonstrating strong discriminative capability and balanced classification12 performance. This work highlights the potential of hybrid fuzzy–ensemble learning for uncertainty-13 aware predictive analytics in biomedical decision support, aligning with the journal’s focus on14 information processes, intelligent systems, and data mining.