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There are considerably more breast cancer fatalities each year. The most common kind of cancer and the main cause of death in women worldwide is this one. A healthy life depends on every development in the prognosis and diagnosis of cancer sickness. The standard of treatment and patient survival rate must be updated, thus an accurate cancer prognosis is crucial. Research has demonstrated that machine learning approaches are effective for the early detection and prediction of breast cancer and have grown in popularity. Random Forest, Logistic Regression, Xtreme Gradient, and AdaBoost Classifier are trained on the Breast Cancer Wisconsin Diagnostic dataset, and their efficacy is assessed and compared in this study using ensemble classifier and machine learning. The major objective of this study is to identify the most effective ensemble and machine learning classifiers for breast cancer detection and diagnosis in terms of Accuracy and AUC Score.


Prediction Breast cancer Machine Learning Ensemble Classifier

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