BCRecommender System for Breast Cancer Diagnosis using Machine Learning Approaches

Abstract

Abstract Background: Early detection of breast cancer is challenging and necessitates more thorough observation. Researchers and medical professionals are always looking for ways to detect breast cancer early, systematically, and affordably. Other studies on cancer have developed a range of recommendation systems, such as those for food and medicine, disease prediction based on text and image data, and prognosis. The only drawback to such systems is that they are highly specialized and only accept specific types of data. To be more precise in diagnosis, we need systems that consider all aspects of the disease and provide continuous support at all the stages of breast cancer. Methods: A recommendation model called "BCRecommender" has been put forth to diagnose and comprehend breast cancer using clinical and histopathological slides while taking feature-based classification into consideration. It has a layered architecture that accepts different reports (structured and unstructured data), and it produces results differently with respect to each layer. It is effective for cancer in all stages, from early to advanced.Existing breast cancer repositories are used to design a system. Results & Conclusions:The model performance varies at different layers.In layer1 the bagging classifier achieves the highest accuracy of 61.06% while in layer2 bagging has the highest accuracy of 97.52%. Layer 3 has an accuracy of 97.39% after augmentation. During the test phase, confidence ranges from 60%-100%.In Layer 4 confidence varies from 50%-100%.