Fuzzy classifiers in cardiovascular disease diagnostics: Review
https://doi.org/10.29001/2073-8552-2020-35-4-22-31
Abstract
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.
Supporting agencies: this work was supported by the Ministry of Science and Higher Education of the Russian Federation (FEWM-2020-0042).
About the Author
I. A. Hodashinsky
Tomsk State University of Control Systems and Radioelectronics
Russian Federation
Russian Federation
Ilya A. Hodashinsky, Dr. Sci. (Tech.), Professor, Department of Integrated Cybersecurity of Electronic Computer Systems
40, Lenin ave., Tomsk, 634050
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Review
For citations:
Hodashinsky I.A. Fuzzy classifiers in cardiovascular disease diagnostics: Review. Siberian Journal of Clinical and Experimental Medicine. 2020;35(4):22-31. (In Russ.) https://doi.org/10.29001/2073-8552-2020-35-4-22-31
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