Breast Cancer Classification Using Gradient Boosting Algorithms Focusing on Reducing the False Negative and SHAP for Explainability

arXiv:2403.09548v1 Announce Type: new
Abstract: Cancer is one of the diseases that kill the most women in the world, with breast cancer being responsible for the highest number of cancer cases and consequently deaths. However, it can be prevented by early detection and, consequently, early treatment. Any development for detection or perdition this kind of cancer is important for a better healthy life. Many studies focus on a model with high accuracy in cancer prediction, but sometimes accuracy alone may …

abstract algorithms arxiv boosting cancer cases classification cs.lg detection diseases explainability false gradient however negative q-bio.qm responsible shap type women world