Diagnose Breast Cancer Using Two Machine Learning Methods andComparing and Analyzing Them

Abstract

The breast cancer is currently the number one cancer in the world and has a high early treatment rate. Therefore, early screening and diagnosis of breast cancer is very important. Machine learning is very strong in analyzing and processing massive data. In this paper, we train and optimize decision tree and SVM models to identify malignant breast cancer and compare and analyze the performance of the two models. The accuracy of the earliest optimized models reaches about 98%, and it is found that the two models focus on different classification effects for the two samples, which can be further optimized by stacking and other ways. The dataset used to train the models in this paper is from the Wisconsin breast cancer database (WBCD), a benchmark dataset commonly used to compare different algorithms, which is of some significance for exploring the use of machine learning in the field of medical diagnosis.