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The complexity of biological objects makes the development of computerized medical systems a difficult algorithmic decision due to the natural uncertainty inherent in these objects. Human thinking is based on vague and approximate data that can be analyzed to form clear decisions. An exact mathematical model of biological objects may not exist in practice, or such a model may be too complex to implement. In this case, fuzzy logic is a suitable tool for solving the specified problem. The problem of medical diagnosis can be viewed as a classification problem. The article presents a literature review of the use of fuzzy classifiers in diagnostics of cardiovascular diseases. The main advantage of fuzzy classifiers in comparison with other artificial intelligence methods is the ability to interpret the resulting classification result. The review aims to expand the knowledge of various researchers working in the field of medical diagnostics.

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В работе проведено сравнение эффективности модификаций алгоритма прыгающих лягушек, позволяющих метаэвристике функционировать в бинарном пространстве поиска. Для задачи отбора признаков в нечетком классификаторе опробованы методы, основанные на модифицированных алгебраических операциях, функциях трансформации и операции слияния, а также их комбинации. В эксперименте использован набор данных SVC2004, содержащий большое количество признаков для аутентификации пользователя на основе динамических признаков рукописной подписи. The paper compares the effectiveness of modifications of the shuffled frog leaping algorithm that allow metaheuristics to function in the binary search space. Methods based on modified algebraic operations, transformation functions and fusion operations, as well as their combinations, have been tested for the task of selecting features in the fuzzy classifier. The SVC2004 data set containing a large number of features for user authentication based on dynamic handwritten signature characteristics was used in the experiment.