Machine Learning Technique for Prediction of Breast Cancer

Abstract

Breast cancer is one of the most prominent disease and the second foremost source of death among middle-aged women in the world. Removing of breast tumour by using a surgical treatment and chemotherapy could work excellently if it can be identified as a primary tumour or at an early stage of transmutation, however it is a costly process. The quick development of machine learning techniques continues to burn the medical tomography enthusiasm in implementing to improve the accurateness of tumour findings. To identify breast cancer in the area of machine learning lots of attempts were made, but these techniques are not too accurate. In the proposed Machine Learning Technique for Prediction of Breast Cancer (MLTPBC) is an automated system used to remove a label, pectoral muscles, noise, and identification of cancer. The experimental results of the proposed MLTPBC shows the preferable accuracy over the existing methods.