Open AccessMachine learning as a tool for diagnostic and prognostic research in coronary artery disease
Author(s) -
Б. И. Гельцер,
М. М. Циванюк,
К. И. Шахгельдян,
В. Ю. Рублев
Publication year - 2020
Publication title -
rossijskij kardiologičeskij žurnal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.141
H-Index - 14
eISSN - 2618-7620
pISSN - 1560-4071
DOI - 10.15829/1560-4071-2020-3999
Subject(s) - medicine , machine learning , artificial intelligence , support vector machine , decision tree , artificial neural network , logistic regression , classifier (uml) , bayesian network , coronary artery disease , clinical decision support system , naive bayes classifier , disease , decision support system , computer science
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.