Diagnosis of Breast Cancer Using Supervised Machine Learning Techniques

Abstract

Breast cancer is a kind of disease which is most common among women. Next to lung cancer it is the second foremostcause of cancer death in women. In order to increase the survival rate of patient who are suffering from breast cancer, a prediction of breast cancer recurrence required. With the help of machine learning techniques and advanced technologies, a cancer can be diagnosed, accuracy can be detected and we can improve the performance.Machine Learning is a statistical model and it is  an application of artificial intelligence (AI) .Artificial Intelligence makes the  system the to learn automatically and learn from experience without any explicit program. This paper provides comparison of most popular machine learning techniques that are used for breast cancer detection and diagnosis on Wisconsin Breast Cancer Dataset(WBCD). Comparison is performed on both classification and regression categories of Supervised learning-Support Vector Machine, Random Forest, Decision Tree, Multilayer perception, Linear regression. The result shows Support Vector Machine provides high accuracy under Classification algorithm whereas Multilayer Perception regressor gives less error under Regression algorithm.