Elena Al. Ryabukhina

Due to the introduction of the discipline "Introduction to Modern Information and Intellectual Technologies" into the curricula of medical universities, which includes a section devoted to the basic principles and algorithms of machine learning and artificial intelligence (AI), there is a need to develop appropriate methodological support. The relevance of the research topic is due to the fact that the vast majority of medical students do not have the necessary knowledge in mathematics and programming, which are the theoretical foundations of modern intelligent technologies. This can lead to incorrect interpretation of AI-generated results in their future professional work. Therefore, it is important to consider the specific needs of this category of students when developing teaching materials. The aim of the article is to describe the authors' proposed approach to implementing computer experiments in the laboratory practice of the above-mentioned discipline, which allows students to gain a deeper understanding of the essence of the studied machine learning methods and the conditions for their application. The research methods used include classification methods used in machine learning, experimental design methods, and two-factor analysis of variance, which serves as a basis for evaluating the effectiveness of the proposed approach. The study was conducted with a participation of students from the Medical Institute of Mordovian University. The result is an approach developed by the authors for conducting laboratory work by medical students. This approach includes: studying the basic mathematical formulas underlying a particular machine learning method, performing simple computational tasks on a small amount of data, studying the basic functions of the Python language to implement a specific practical task, and conducting computer experiments to further explore the chosen mathematical model and improve its accuracy. The effectiveness of this approach has been confirmed by the results of a pedagogical experiment conducted by the authors. The theoretical significance of this work lies in the generalization and systematization of materials on this topic, and the results obtained complement existing scientific and pedagogical developments, deepening our understanding of the processes related to this topic, and they can be used in further research. The practical significance lies in the fact that the presented approach can be used as a basis for developing methodological materials on the topic "Machine Learning and Artificial Intelligence Technologies in Healthcare."