A Comparative Study of Machine Learning Algorithms for Use in Breast Cancer Studies
Chuck Easttom, Sudip Thapa, Justin Lawson · 2020 10th Annual Computing and Communication Workshop and Conference (CCWC) · 2020
This current study examines a range of machine learning algorithms efficacy at determining malignancy from breast cancer imagery. The study uses the Wisconsin data set (diagnostic) to evaluate the algorithms performance. While there are a number of published studies regarding machine learning and breast cancer imagery data, there are gaps in the current literature. One gap is that several studies focus on a single algorithm. Another gap is that comparative studies often focus on only a few algorithms. The current study addresses these specific gaps in the existing literature. The current study looks at 5 different algorithms, then compares those to 8 algorithms from the literature. This provides a broad comparative study of the efficacy of machine learning algorithms in detecting breast cancer.