Classifying Malignant and Benign Tumors of Breast Cancer

Breast cancer is the second major cause of deaths in women. Machine learning classification techniques can be used to increase precision diagnosis and bring it closer 100%, thus saving lives many people. This paper proposed four different models, built using combinations selected features applying five ML all models identify best model with highest accuracy. It analyzes machine techniques, namely logistic regression (LR), support vector machines (SVM), naive bayes (NB), decision trees (DT), k-nearest neighbor (KNN), for prediction breast Wisconsin Diagnostic Cancer Dataset on these models. The objective find algorithm that most accurately predict a particular model. outcome this helps doctors improvise by knowing effect symptoms growth cancer.