TL;DR
The results indicate that the clinically curated feature set is highly informative and exhibits low redundancy, and that RST primarily serves to validate feature sufficiency while enabling modest model simplification.
A clear, statistically sound, yet easily understandable breast cancer diagnosis is a difficult issue in all healthcare systems, because early stages of breast cancer are critical in therapy success and long-term survivability. This machine-learning-based breast cancer classifier, in a statistically justified, rigorousl...
This study bridges the translational gap between predictive accuracy and clinical utility by developing an explainable artificial intelligence (XAI) framework specifically designed for breast cancer diagnosis in underserved healthcare settings, and delivers a reproducible, transparent framework whose SHAP-derived signa...
The proposed framework can serve as an intelligent decision-support tool for prioritizing breast cancer patients and improving resource allocation when healthcare capacity is constrained and its relatively simple and scalable architecture facilitates potential implementation in healthcare environments with limited reso...
Accurate identification of HER2-positive breast cancer is essential for treatment planning; however, diagnostic decision-making in resource-constrained healthcare systems is challenged by class imbalance, which can substantially reduce the detection of clinically important minority-class cases. Although numerous imbala...
Breast cancer remains one of the leading causes of mortality among women worldwide, underscoring the critical need for effective and early diagnostic tools. This study presents a comprehensive Machine Learning (ML) framework that employs k-Nearest Neighbors (KNN), Random Forest (RF), Logistic Regression (LR), and Extre...
Although the evaluated LLMs did not outperform traditional supervised models, the study provides a clear performance baseline for future research on structured clinical prediction with language models and shows that LLMs may offer value as complementary exploratory tools, but their outputs should be interpreted only wi...
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