Abstract
This study aims to develop a methodology for the justification of medical diagnostic decisions based on the clustering of large volumes of statistical information stored in decision support systems. This aim is relevant since the analyzed medical data are often incomplete and inaccurate, negatively affecting the correctness of medical diagnosis and the subsequent choice of the most effective treatment actions. Clustering is an effective mathematical tool for selecting useful information under conditions of initial data uncertainty. The analysis showed that the most appropriate algorithm to solve the problem is based on fuzzy clustering and fuzzy equivalence relation. The methods of the present study are based on the use of this algorithm forming the technique of analyzing large volumes of medical data due to prepare a rationale for making medical diagnostic decisions. The proposed methodology involves the sequential implementation of the following procedures: preliminary data preparation, selecting the purpose of cluster data analysis, determining the form of results presentation, data normalization, selection of criteria for assessing the quality of the solution, application of fuzzy data clustering, evaluation of the sample, results and their use in further work. Fuzzy clustering quality evaluation criteria include partition coefficient, entropy separation criterion, separation efficiency ratio, and cluster power criterion. The novelty of the results of this article is related to the fact that the proposed methodology makes it possible to work with clusters of arbitrary shape and missing centers, which is impossible when using universal algorithms. Doi: 10.28991/esj-2021-01305 Full Text: PDF
Highlights
Information processes, primarily including information flows transition, are increasingly affecting the practices of medicine and health care
Making effective medical decisions in the diagnosis, treatment, organization, and management of health issues requires a substantial amount of statistical information that must meet the requirements of reliability, completeness, relevance, and availability
The necessary data relate to patient characteristics, medical services, and healthcare facility management
Summary
Information processes, primarily including information flows transition, are increasingly affecting the practices of medicine and health care. The medical decision-making adequacy is due to the systematic accumulation of significant volumes of diverse and various (including semi structured or poorly formalized) medical statistical information for all types of processes and problems, and to its proper analysis and processing, aimed for reasonable selecting data sets. It is relevant to solve a set of practical problems aimed at MDSS implementation These include formalizing the problem solution of preparing a rationale for selecting the most appropriate medical diagnostic decision (MDD) from the list of recommended options.
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