Breast Cancer Detection Using Artificial Intelligence

Breast cancer is one of the most common diseases faced by women and causes more deaths every day. It is considered one of the greatest diseases in medical history. Automated diagnostic systems not only assist diagnosticians but also provide reliable, efficient and fast services that reduce the likelihood of death. This paper proposes a model to manually classify breast cancer samples into benign and malignant subcategories using multiple algorithms to predict interpretation. It is generally considered as all data containing a set of 30 characteristics related to the nuclei found in digitized images of fine needle aspiration (FNA) of the breast. All acquired features are visualized and understood to gain insight for future diagnoses. Breast cancer prediction uses support vector distribution (SVC) in its ability to identify new, unrecognized data based on a set of features.