AbstractBreast Cancer (BC) is the second leading cause of death among women throughout the world. Early-stage identification of breast abnormality helps the people to attend better treatment at a premature stage of tumour. Breast abnormality detection and risk rate prediction will support the people to increase the survival rate of a patient. Machine learning (ML) techniques have a long track record in the healthcare domain and especially in disease risk classification. In this article, an innovative Internet of Things (IoT) based intelligent- breast abnormality detection (I-BAD) framework to monitor and collect various breast health vital parameters is proposed and evaluated the efficiency of different machine learning techniques in breast abnormality classification.