Comparison of Different Machine Learning Models in Breast Cancer

Abstract

Breast Cancer is mainly found in women and is the main cause of increased mortality among women. Breast cancer diagnosis is time-consuming, and due to the low availability of the system, it is necessary to develop a system that can automatically diagnose breast cancer at an early stage. Various machine learning and Deep Learning Algorithms have been used to classify benign and malignant tumors. This paper focuses on the implementation of various models, such as Logistic regression, random forest and naive Bayes. Each algorithm has measured and compared the accuracy and obtained accuracy. This paper aims to compare the advantages and disadvantages of different regression models in breast cancer prediction. The method proposed in this paper can promote the integration of machine learning and medicine, and improve clinical diagnostic accuracy.