Machine learning methods as a tool for diagnostic and prognostic research in cardiovascular disease
Abstract
Machine learning (ML) methods are the main tool of artificial intelligence, the use of which makes it possible to automate the processing and analysis of big data, to reveal hidden or non-obvious patterns on this basis, and to extract new knowledge. The review presents an analysis of scientific literature on the use of ML methods for diagnosing and predicting the clinical course of coronary heart disease. Provides information on reference databases, the use of which allows you 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 models based on ML methods and their introduction into clinical practice will help support medical decision-making, improve the effectiveness of treatment and optimize health care costs.