A DATA DRIVEN BREAST CANCER DETECTION METHOD

Abstract

One of the most common chronic diseases is breast cancer. Breast cancer is not only widespread, but it is also quite complicated. Treating patients with Breast cancer demands doctors to examine enormous amount of data – often too much for human clinicians to analyze on their own. To accelerate the development of better Breast cancer treatments, a big data analytics model can quickly draw meaningful insights and identify cancer-related patterns in the data which radiologists cannot. Big data analytics can help enhance the accuracy of breast cancer screenings, reduce the number of biopsies needed and increase physicians’ confidence in the accuracy of assessments made for screening exams. This article proposes a big data analytics model in breast cancer detection which is capable of processing an enormous amount of data quickly to assist in the early detection of breast cancer with better accuracy. This will augment care delivery, diagnostics and reduce the death rate. In this work, we build a model based on Machine Learning Algorithm to predict breast cancer. The result shows that the machine learning model based on big data analytics can predict better than traditional models, which currently incorporate only a small fraction of patient data.