Breast Cancer Prediction based on Support Vector Machine

Abstract

Breast cancer is the most common malignant tumor among women in the world, and its mortality rate ranks second. In this paper, the breath cancer Wisconsin (diagnostic) data set is used for prediction. First, exploratory data analysis was carried out, and 10 indicators related to breast cancer were selected as independent variables. Then, support vector machine (SVM) and logistic regression model were used as classifiers, and 75% of data sets were divided into training sets to build models. Finally, 25% of test sets were used as input models for prediction, and accuracy and recall were used as evaluation indicators to compare and analyze the advantages and disadvantages of the two algorithms. The results show that the accuracy of SVM is 16.2% higher than that of logistic regression, and the recall rate is 9.2% lower. The prediction application of SVM algorithm shows that SVM algorithm has low computational complexity and strong generalization ability. Therefore, support vector machine can be used to improve the diagnostic accuracy in the diagnosis of breast tumors.