Breast cancer remains one of the most common and life-threatening diseases among women worldwide. Early detection and accurate prognosis play a vital role in improving patient survival rates. This paper focuses on the diagnosis and prognosis analysis of breast cancer using machine learning and data mining classification techniques. The proposed approach employs algorithms such as Decision Trees, Support Vector Machines (SVM), Random Forest, and Naïve Bayes to classify tumor types as benign or malignant based on clinical and histopathological data. Data preprocessing, feature selection, and normalization are performed to enhance model accuracy and reduce computational complexity. The models are trained and validated using benchmark datasets such as the Wisconsin Breast Cancer Dataset (WBCD). Comparative analysis demonstrates that ensemble and hybrid models achieve higher prediction accuracy and robustness than traditional methods. The results highlight the potential of machine learning-based classification in assisting medical professionals with early diagnosis, treatment planning, and personalized healthcare.
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