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+### Abstract
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+This study explores a range of machine learning techniques, including both regression and classification models,to predict these outcomes based on patient data. Classification models like Random Forest, Support Vector Machine (SVM),Logistic Regression and Decision tree were employed to classify patients into predefined categories, such as responder or nonresponder to treatment. Additionally, regression models wereutilized to predict continuous outcomes, offering insights intothe exact degree of response or survival time. These modelswere chosen for their robustness, interpretability, and ability to capture complex, non-linear relationships within the dataset.The methodologies involve mathematical formulations such as
+optimization of decision boundaries in SVM, loss function minimization in XGBoost, and error reduction in regression tasks.By integrating both regression and classification approaches, this study provides a comprehensive framework for predicting breast cancer outcomes, aiding clinicians in making informed, datadriven decisions.
+