Machine Learning Base Methods for Breast Cancer Diagnose

Abstract

Cancer is a serious threat to people's health, and its heterogeneous nature and its ability to divide and proliferate make it difficult to cure. For women around the world, breast cancer has been affecting their health and even the risk of life. Therefore, earlier and more accurate diagnosis can save patient's lives. As research into machine learning has become more advanced, different algorithms have been applied to various datasets, including medical data. In this paper, mainly introduce three algorithms that are commonly used and superior in cancer diagnosis, K-Nearest Neighbor algorithm, Naive Bayesian algorithm based on Bayes' theorem and Support Vector Machine. An experimental case is used to illustrate the F1 score, accuracy and recall rate of these two algorithms on the same data set.