Survival Outcome Prediction for Breast Cancer Patients

Abstract

The second most causative disease is breast cancer happening in women and a significant explanation behind expanding death rate among women. Observed rates of this cancer are increasing with industrialization and also with early detection facilities. As the finding of this ailment physically takes extended periods and the lesser accessibility of frameworks, there is a need to build up the programmed determination framework for early identification of malignant growth. We have used machine learning classification techniques to categorize benign and malignant tumors, in which the machine learns from past data and predicts the new input category. Models like logistic regression and Random Forest are Done on the UCI dataset. Our experiments have indicated that Random Forest has the best prescient examination with exactness of ~96%.