Fuzzy model for intellectualizing medical knowledge

Abstract

EN: Context. The research is devoted to the development of a flexible mathematical apparatus for the intellectualisation of knowledge in the medical field. As a rule, human thinking is based on inaccurate, approximate data, the analysis of which allows us to formulate clear decisions. In cases where there is no exact mathematical model of an object, or the model is difficult to implement, it is advisable to use a fuzzy logic apparatus. The article is aimed at expanding the range of knowledge of researchers working in the field of medical diagnostics. Objective. The aim of the study is to improve the quality of reflection of the subject area of the medical sphere on the basis of building type-2 fuzzy knowledge bases with interval membership functions. Method. The article describes an approach to formalising the knowledge of a medical specialist using second-order fuzzy sets, which allows taking into account the uncertainty and vagueness inherent in medical data and solving the problem of interpreting the results obtained. Results. The developed approach is implemented on a specific problem faced by an anaesthetist when admitting a patient to elective (planned) surgery. Conclusions. Experimental studies have shown that the presented type-2 fuzzy model with interval membership functions allows to adequately reflect the input medical variables of a qualitative nature and take into account both the knowledge of a specialist in medical practice and research medical and biological data. The acquired results hold substantial practical importance for medical practitioners, especially anesthetists, as they lead to enhanced patient assessments, error reduction, and tailored recommendations. This research fosters the advancement of intelligent systems capable of positively influencing clinical practices and improving patient outcomes within the realm of medical diagnostics