Methods for predicting the risks of developing cardiovascular diseases

Forecasting the risks of developing cardiovascular diseases (CVD) is one of the priority tasks of modern preventive medicine. One of the promising areas is the use of machine learning methods and mathematical modeling, which allows considering the combined impact of diverse risk factors. Aim of the study was to summarize and analyze research data devoted to methods for predicting the risk of developing CVD.

Material and methods. The information search was carried out in the PubMed, eLibrary, and CyberLeninka databases for the period from 1961 to 2025. A total of 820 sources were analyzed, of which 68 were included in the review.

Results and discussion. The review provides an analysis of methods for predicting the development of chronic non-communicable diseases (CVD, prognosis of the ten-year risk of developing CVD and death from them). The development of this area in foreign and domestic medicine is shown in chronological order and the most common methods for predicting the development of circulatory system diseases are presented. The article provides argumentation for the active use of artificial intelligence methods in the field of preventive medicine, which allow processing big data and taking into account various combinations of risk factors.

Conclusions. To ensure high accuracy of prognostication of CVD, it is important to consider a wide range of risk factors (ethnic, socioeconomic, cultural, behavioral, medical and biological, production process factors) that operate within the framework of a separately studied group.

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