INVESTIGATION OF DATA ON MORPHOLOGICAL CLASSIFICATION OF CLINICAL AND HEMATOLOGICAL SYNDROMES BASED ON MACHINE LEARNING ALGORITHMS

Abstract

Currently, the greatest attention is paid to the problem of the development and application of medical information systems, their integration in the direction of building a single information space. There is a need to develop decision support systems that are direct «assistants» of doctors in the medical and diagnostic process and should find their place in integrated systems. The urgency of the problem lies in the need to develop intelligent medical systems based on effective methods, algorithms and models to support medical decision- making in conditions of incompleteness and uncertainty of the initial data of the medical and technological process, allowing to ensure high adequacy and validity of decisions made in conditions of limited time resources. To solve this problem, this article proposes a study of data on the morphological classification of clinical and hematological syndromes based on machine learning algorithms. The use of machine learning algorithms for indicators of clinical and hematological sy ndromes will increase the effectiveness of differential diagnosis and apply it to the development of algorithmic and software for an intelligent system to support clinical decision-making.