Comparison of Machine Learning Methods for Breast Cancer Diagnosis

Abstract

Cancer is the common problem for all people in the world with all types. Particularly, Breast Cancer is the most frequent disease as a cancer type for women. Therefore, any development for diagnosis and prediction of cancer disease is capital important for a healthy life. Machine learning techniques can make a huge contribute on the process of early diagnosis and prediction of cancer. In this paper, two of the most popular machine learning techniques have been used for classification of Wisconsin Breast Cancer (Original) dataset and the classification performance of these techniques have been compared with each other using the values of accuracy, precision, recall and ROC Area. The best performance has been obtained by Support Vector Machine technique with the highest accuracy.