Using machine learning methods in problems with large amounts of data

¹ Reshetnev Siberian State University of Science and Technology, 31, Krasnoyarsky Rabochy Av., 660037 Krasnoyarsk, Russian Federation
² Siberian Federal University, 79, Svobodny Av., Krasnoyarsk, 660041, Russian Federation

^* Corresponding author: olga-barishnikova@yandex.ru

Abstract

This article explores the use of artificial intelligence in medicine, in particular in radiology, pathology, drug development. The usefulness of robotic assistants in the medical field is revealed, including machine learning in medical science, as well as routing in hospitals. It also discusses such machine learning methods as classification methods, regression restoration methods, clustering methods. As a result, based on what is considered in this article, it is concluded that manual processing becomes more complicated and impossible with a large amount of data. There is a need for automatic processing that can transform modern medicine. And also, conclusions were made about how accurately the deep learning mechanisms can provide a more accurate result in the processing and classification of images compared to the results obtained at the human level. It became clear that deep learning not only aids in the selection and extraction of characteristics, but also has the potential to measure predictive target audiences and provide proactive predictions to help clinicians go a long way.