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Original Article

. 2020; 11(2): 218-223


The Algorithm for Selection of Symptom Complex of Ischemic Heart Diseases Based on Flexible Search

Nishanov Akhram Khasanovich, Akbaraliev Bakhtiyorjon Bakirovich, Juraev Gulomjon Primovich, Khasanova Malika Akhramovna, Maksudova Malika Khamdamjanovna, Umarova Zamira Fakhriyevna.

Abstract
In the case of preliminary data processing, particularly, in identifying symbols, the determination and categorization of informative symbols or sets of symbols that classify objects is an important issue. Although, there have been a number of methods and algorithms proposed to solve these problems, many more ones are to be solved in this direction. This is due to the fact that most of the proposed approaches are strongly dependent on the nature of the subject of the study, the number of symbols, the type of values that can take symbols, the size of the training sample, and so forth. In addition, there are certain requirements for the above. Besides these, each method or algorithm depends to a large extent on the correctness of the information selection criterion and choosing the appropriate decisive rule that determines the quality of the selected choice. In this regard, the efficiency and reliability of many methods and algorithms are not stable.
The flexible search-based algorithm proposed below is in some sense universal, since symbols can take different types of values, and the proposed informative criterion for selecting a set of symbols is based on minimizing classification errors. In addition, the probability vector used in character selection prevents objects from irrelevant displacement of their important characters out of the selection.
Using this algorithm, the diagnostic data of patients with stenocardia, acute myocardial infarction and cardiac arrhythmia, which are part of ischemic heart disease, were first processed. In this case, the problem of selecting and classifying disease complexes was studied according to the clinical signs and symptoms of patients. Results of the study are presented.

Key words: preliminary data processing, criteria for informativeness, character selection, classification error coefficient, flexible random search.



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