I.A Mishkin1,A.V. Kontsevaya,A.V Gusev,O. M. Drapkina
Relevance. Every year, a large number of people around the world become victims of cardiovascular diseases. To date, the main tools for predicting cardiovascular risk are scales based on proportional
risk models (Cox regression). However, recently many scientists agree that the use of machine learning and artificial intelligence technologies can help to improve the quality of adverse cardiovascular events onset prognosis.
The aim is to conduct a systematic literature review of approaches to the formation of CVD development forecasts based on proportional risk assessment scales and ML methods to identify the most effective methods of data analysis.
Materials and methods: A systematic review of the literature was conducted, which included 58 research papers using methods for assessing cardiovascular risk based on Cox regression and machine
learning technologies.
Results. Predictive capabilities of machine learning are superior to traditional linear methods of data analysis. The…
