Machine learning as a tool for diagnostic and prognostic research in coronary artery disease
https://doi.org/10.15829/1560-4071-2020-3999
Abstract
Machine learning (ML) are the central tool of artificial intelligence, the use of which makes it possible to automate the processing and analysis of large data, reveal hidden or non-obvious patterns and learn a new knowledge. The review presents an analysis of literature on the use of ML for diagnosing and predicting the clinical course of coronary artery disease. We provided information on reference databases, the use of which allows to develop models and validate them (European ST-T Database, Cleveland Heart Disease database, Multi-Ethnic Study of Atherosclerosis, etc.). The advantages and disadvantages of individual ML methods (logistic regression, support vector machines, decision trees, naive Bayesian classifier, k-nearest neighbors) for the development of diagnostic and predictive algorithms are shown. The most promising ML methods include deep learning, which is implemented using multilayer artificial neural networks. It is assumed that the improvement of ML-based models and their introduction into clinical practice will help support medical decision-making, increase the effectiveness of treatment and optimize health care costs.
Supporting agencies: This work was supported by grants from the Russian Foundation for Basic Research within the projects № 18-29-03131 and № 19-29-01077
About the Authors
B. I. Geltser
Far Eastern Federal University
Russian Federation
Competing Interests: not
Russian Federation
Boris Geltser
Vladivostok
Competing Interests: not
M. M. Tsivanyuk
Far Eastern Federal University
Russian Federation
Competing Interests: not
Russian Federation
Mikhail Tsivanyuk
Vladivostok
Competing Interests: not
K. I. Shakhgeldyan
Far Eastern Federal University
Russian Federation
Competing Interests: not
Russian Federation
Karina Shakhgeldyan
Vladivostok
Competing Interests: not
V. Yu. Rublev
Far Eastern Federal University
Russian Federation
Vladivostok
Competing Interests: not
Russian Federation
Vladivostok
Competing Interests: not
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For citations:
Geltser B.I., Tsivanyuk M.M., Shakhgeldyan K.I., Rublev V.Yu. Machine learning as a tool for diagnostic and prognostic research in coronary artery disease. Russian Journal of Cardiology. 2020;25(12):3999. (In Russ.) https://doi.org/10.15829/1560-4071-2020-3999
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