DEVELOPMENT OF THE STRUCTURE OF AN AUTOMATED SYSTEM FOR DIAGNOSING DISEASES

Abstract

Today, the importance of information support for various medical technologies has increased significantly. The use of modern information technologies is becoming a critical factor in the development of most branches of knowledge and areas of practice, so the development and implementation of information systems is an urgent task. The clinical decision support system provides clinicians and stakeholders with individualized patient assessments and recommendations to assist in the clinical decision-making process. Knowledge-based information systems are widely used in medicine around the world. Modern technical capabilities make it possible to reach a qualitatively new level of presentation of the course of the disease, namely, based on appropriate mathematical models, to model the typical development of the pathological process in a particular disease, to speed up the process of diagnosing and receiving recommendations on treatment protocols. In Kazakhstan, there is no variety of decision support systems in medicine, especially in the process of diagnosing diseases. The purpose of this study is to develop the structure of an automated system for diagnosing diseases. The Unified Modeling Language (UML) is used as a design tool. The structure of the automated system is presented; the main components and key terms are considered. A mathematical model of diagnostics is shown (in the example of diseases of intestinal and pancreatic insufficiency) based on decision-making methods with fuzzy initial data. A diagram of the data flows of the system is presented. Thus, the paper proposes the structure of an automated system that will contribute to high-quality diagnostics due to an effective method of system organization and the use of fuzzy set theory methods.

More by Kateryna Kolesnikova

• Digital Twin-Enabled Framework for Risk Assessment in Industrial IoT Network TopologiesLecture notes in mechanical engineering · 2026
• Morpheme-Aware Interpolated N-gram Language Modeling for Low-Resource Speech RecognitionEmerging Science Journal · 2026
• The Limits of External Observability: Evidence Survival from Public Hosts to CVEs in Exposed IoT/IIoT SystemsComputers · 2026
• KAZMORPHLM: MORPHEME-AWARE LANGUAGE MODEL FOR KAZAKH AUTOMATIC SPEECH RECOGNITIONScientific Journal of Astana IT University · 2026