Breast Cancer Detection using Machine Learning

Breast Cancer is the major cancer among all other type of cancer in women around the world and it is increasing day by day in most of the developing countries and the cases are diagnosed in the last stage in most cases. Some of the project that have been already proposed shows a comparison with the help of machine learning algorithms like data mining techniques or blood analysis, ensemble methods and diagnostics etc. In this paper we will make a comparison of six machine learning (ML) algorithms: random forest (RT), Naive Bayes (NB), nearest neighbor (KNN), artificial neural networks (ANN), support vector machines (SVM) and Wisconsin Decision Tree (DT) on Diagnostic Breast Cancer (WDBC) dataset which is extracted from a digitized image of an MRI. Some data from the international agencies shows that breast cancer generally occurs in the middle-aged women and there are very few cases where it is seen in the women under the age of 25.