Determine the Degree of Malignancy in Breast Cancer using Machine Learning

It is predicted that 2.1 million women were given a diagnosis of breast cancer for the first time in 2018, and that 626,679 women who had breast cancer passed away because of the disease. Breast cancer is the most common form of cancer among women globally. Even though there is a wide variety of approaches to determining the presence or absence of malignancy, there are several procedures that raise concerns due to their varying degrees of accuracy in diagnosis [3]. As a result of the precision and speed of computer-assisted diagnostics, machine learning is receiving an increasing amount of attention for its potential role in aiding with illness prediction in this field. For this work, we used several different machine learning methods to investigate breast cancer malignancies. To evaluate whether breast tissue cells acquired by fine needle aspiration (FNA) were malignant, these methods were used to a diagnostic dataset of characteristics from those cells. We observed that the k-nearest neighbor (KNN) model had the greatest performance of all the models that were evaluated and scored the maximum accuracy. This was determined by the creation of confusion matrices and the evaluation of several different metrics (97.66 percent). We were also successful in developing a simpler generalized additive model (GAM) that depended on just three characteristics but achieved an accuracy level that was like the original model (95.91 percent). R Studio was used for all the tests that were conducted.