Analysis of Breast Cancer Prognosis Using Supervised Machine Learning Classifiers

Abstract

This paper entails an approach to analyze the Breast cancer prognosis using supervised machine learning classifiers with data preprocessing techniques to handle the unbalanced data and improve the accuracy and kappa statistics of the classifier. The research findings will help in medical diagnosis of the breast cancer and hence accordingly a predictive model can be designed for computer based medical diagnosis and prediction systems. In this research work LMT classifier has provided the best results on unbalanced data, while Multilayer Perceptron Function has showed the improved accuracies after applying various data preprocessing techniques which includes class balancer, re-sampling and SMOTE.