A Breakup Machine Learning Approach for Breast Cancer Prediction

Abstract

Breast cancer is one of the most common cancers and is the major cause of cancer-related deaths in women worldwide. Breast cancer is a disease in which cells in the breast grow in a rapid state and out of control. Breast cancer can grow in different parts of the breast and may or may not spread outside the breast. If caught on early stage, the breast cancer can be cured before they spread but if the cancer cells have spread to other parts of the body, usually it is hard to cure. Early diagnosis of breast cancer might help increasing the life-span of cancer affected women. In this paper, a novel method for prediction of breast cancer, that enhances the accuracy using machine learning is proposed using six machine learning algorithms. The unbiased estimates of the algorithms are measured using k-fold cross-validation method. This proposed approach proves to increase the accuracy of traditional machine learning algorithms.