Application of machine learning technologies for establishing a medical diagnosis

DOI: 10.31673/2412-9070.2024.051397

Abstract

This article provides a detailed analysis and description of the methods used to use machine learning technologies in medical diagnoses. This is an important aspect, since the development of machine learning in medicine not only changes approaches to diagnostics, but also contributes to the creation of new approaches to treatment, improvement of the quality of medical services, and optimization of decision-making processes. Paradigms of machine learning, such as supervised learning, unsupervised learning, and reinforcement learning, are given and examples of their use in the field of medicine are presented. The main types of data used to train machine learning models in medical practice are analyzed, such as clinical data, medical images, information about the patient's genome. Scientific approaches to the application of machine learning technologies in medical diagnostics are considered, namely logistic regression, support vector method (SVM), decision trees and Random Forest, artificial neural networks (ANN), deep neural networks (Deep Learning), convolutional neural networks (CNN), linear and polynomial regression, and natural language processing (NLP). It has been proven that modern medicine largely depends on the latest technologies to improve the quality of diagnosis and treatment of patients. Recommendations for the application of machine learning
technologies have been developed, which will allow automating the diagnostic processes and increasing the accuracy of treatment results. The use of machine learning in the medical field opens up new opportunities for processing large volumes of data to determine the diagnosis, improve the efficiency of clinical decision-making, and generally simplify the work of doctors.

Keywords: machine learning, information technologies, artificial intelligence, neural net-works, information system, model, support vector method.