Abstract: Nowadays, breast cancer is a common problem appearing in women. According to statistics, by the end of 2020, there were 7.8 million women alive, who were diagnosed with breast cancer in the past 5 years. Making it the world’s most prevalent cancer. Although, predicting diagnosis & prognosis in breast cancer are two clinical applications that have represented a test to the scientists. But the utilization of AI and data mining strategies has changed the entire course of breast malignancy Diagnosis and Prognosis. Breast Cancer Diagnosis recognizes harmless from dangerous breast lumps and Breast Cancer Prognosis predicts when Breast Cancer is probably going to repeat in patients that have had their malignancies existed. Accordingly, these two issues are primarily in the extent of the grouping issues. Most data mining strategies that are regularly utilized in this space are considered as order classification and applied expectation methods allocate patients to either a harmless gathering that is non-cancerous or a” malignant” group that is cancerous. Consequently the breast malignancy demonstrative issues are fundamentally in the extent of the generally examined grouping issues. In this review, amazing order calculations to be an Artificial Neural Network has been applied for breast cancer prediction. Experiment results show that the previously mentioned calculations have a promising outcome for this reason with the general expectation precision of 96%, individually. A model can be built for each stage reflecting effect due to patient characteristics.

Keywords: Predicting Breast Cancer, Diagnosis Breast Cancer, Prognosis Breast Cancer, Artificial Neural Network, Machine Learning, Data Mining.

| DOI: 10.17148/IARJSET.2021.81123