Comparison between Bayesian and SVM model for breast cancer risk prediction

Abstract

Breast cancer ranks first in the incidence of cancer worldwide. It is a kind of malignant disease with incidence increasing year by year. Recently, machine learning algorithms widely used in cancer prediction and other fields to relieve the burden of doctors and accelerate the diagnostic process. In this work, two representative models, the support vector machine (SVM) and Bayesian classification algorithm are leveraged for breast cancer risk prediction. It is found that the effect and accuracy of the two algorithms are very different when they are used for breast cancer prediction. However, there is still a research gap in the comparison of the two algorithms in practical application. Therefore, the research topic of this paper is the comparison between the Bayesian classification algorithm and the SVM algorithm in breast cancer prediction. The research methods of this paper are as follows: firstly, the dataset is collected, then applying the Bayesian classification to process the dataset, and then the SVM algorithm is used to process the dataset. Finally, the processing results of the two algorithms are compared to comprehensively analyze and compare which algorithm is more efficient and more suitable for actual breast cancer prediction. The test results support that the SVM outperforms the Bayesian classification algorithm in the actual target tracking problem. Therefore, it is suggested to choose the SVM classification algorithm in the actual target tracking problem to boost the accuracy and efficiency of prediction results to the greatest extent.