Abstract
Cardiovascular aging poses a significant threat to the health and quality of life of individuals, especially those aged 65 years and older. This paper presents a way to predict cardiovascular aging using mathematical modeling. The developed model integrates various physiological and behavioral factors including blood pressure, cholesterol level, body mass index, smoking, physical activity and alcohol. The model is based on the application of iteration and Runge Kutta methods, which allows us to describe the dynamic interaction of these factors over time. Validation of the model was performed based on data from clinical studies of elderly patients' health. The results show that the model has high accuracy in predicting the progression of cardiovascular aging and allows to identify patients with increased risk of cardiovascular diseases. The proposed prediction method may become a valuable tool for physicians, helping to develop personalized prevention and intervention strategies in geriatrics, which, in turn, may improve treatment outcomes and prolong the healthy life of patients. Further refinement of the model parameters and expansion of its application to broader populations are planned for the future.