Using Machine Learning Methods for Breast Cancer Diagnosis

In recent years, machine learning methods have been attracting increasing attention in the field of diagnostics, especially in the detection of breast cancer. The aim of this review is to analyze current research on the use of machine learning methods in breast cancer diagnosis in three directions: their use in solving auxiliary tasks that arise in modern breast cancer diagnostics, their role in providing an intelligent assessment of the patient's condition for preliminary diagnostic decisions, and their ability to determine breast cancer risk factors. The analysis has shown that the use of machine learning methods in breast cancer diagnosis provides great opportunities for improving accuracy and efficiency, as well as for solving additional tasks. As a result of the literature analysis, features that are used as input data in machine learning methods have been obtained. In the future, the collected information will be used to construct a feature system for breast cancer diagnosis using machine learning methods.