An Efficient Ensemble Method Using K-Fold Cross Validation for the Early Detection of Benign and Malignant Breast Cancer

In comparison to all other malignancies, breast cancer is the most commonform of cancer, among women. Breast prediction has been studied by a number researchers, and considered as serious threat Clinicians are finding it difficult create treatment approach that will help patients live longer, due lack solid predictive models which predicts outcome in early stages analyzing history patient’s data. Rates this malignancy have observed rise, more with industrialization urbanization, well detection facilities. It still considerably prevalent very developed countries, but rapidly spreading developing countries well. The purpose work offer report on disease we used available technical breakthroughs construct survivability models. Machine Learning (ML) techniques, namely Support Vector (SVM), K-Nearest Neighbors (KNN), Decision Tree (DT) Classifier, Random Forests (RF), Logistic Regression (LR)are base Learners their performance compared ensemble method, eXtreme Gradient Boosting(XGBoost). For comparison, employed k-fold cross validation method measure unbiased estimate these results indicated XGBoost outperformed an accuracy 97.81% ML algorithms.