Breast cancer is a very common and deadly disease. The medical field is looking for minimally invasive ways to diagnose breast cancer, to increase the number of patients that are willing to get checked. One technique that can be used to determine whether a tumor is malignant, is called fine needle aspiration (FNA). It is a biopsy technique that requires no surgery. The data that FNA provides is much more difficult to analyze than a regular biopsy, and the trends in the data are difficult to spot. This project utilizes machine learning techniques and a neural network to create a mathematical model that can predict whether a patient’s tumor is malignant or benign, based on FNA data. A two-step process is used in which a neural network model is
