Open AccessImprovement of cardiovascular risk assessment using machine learning methods
Author(s) -
А.В. Гусев,
Daniil Gavrilov,
R. E. Novitsky,
Т. Yu. Kuznetsova,
S. А. Boytsov
Publication year - 2021
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 - 10.15829/1560-4071-2021-4618
Subject(s) - medicine , machine learning , risk stratification , artificial intelligence , psychological intervention , risk assessment , predictive modelling , computer science , computer security , psychiatry
The increase in the prevalence of cardiovascular diseases (CVDs) specifies the importance of their prediction, the need for accurate risk stratification, preventive and treatment interventions. Large medical databases and technologies for their processing in the form of machine learning algorithms that have appeared in recent years have the potential to improve predictive accuracy and personalize treatment approaches to CVDs. The review examines the application of machine learning in predicting and identifying cardiovascular events. The role of this technology both in the calculation of total cardiovascular risk and in the prediction of individual diseases and events is discussed. We compared the predictive accuracy of current risk scores and various machine learning algorithms. The conditions for using machine learning and developing personalized tactics for managing patients with CVDs are analyzed.