Breast Cancer Classification in IoT Framework

Abstract

In the last decade, Breast Cancer (BC) is one of the widespreading diseases for women and causing of the death in the world wide. Early detection of BC can enhance the changes of survival. Now there are many techniques and methodologies for detecting BC in an appropriate time. Machine learning (ML) technique can be used to observe and classify the abnormalities that happened inside the breast such as micro-calcifications, masses, and asymmetry. With the help of ML techniques, it has become easier to distinguish tumor from the affected breast and diagnose BC. This paper aims to classify breast tumor (benign or malignant) in Internet of Things (IoT) health environment framework. Both Support Vector Machine (SVM) and back-Propagation classifiers are used for classification of breast tumor. In this paper, three performance evaluation metrics are used to evaluate, which are; classification accuracy, sensitivity, specificity.