Classification and Detection of Breast Cancer Using Machine Learning

Abstract

One of the major second largest issues found in the world is breast cancer. For increasing in long term, the continuance of women can be achieved by early and accurate diagnosis. Early diagnosis is the only remedy to prevent breast cancer. Since detection of this disease is a critical issue, this research work focuses on various machine learning methods which can assist doctors in giving promising results in a correct diagnosis of cancer. Thirteen machine learning models are employed and compared on the various measures. Wisconsin Breast Cancer Database (WBCD) dataset is employed in performing the experimentation, which is extract from UCI repository. AdaBoost, logistic regression and 1-NN machine learning models give promising accuracy of 98% in performing the experiment among all the models.