Background: The aging population is increasing rapidly, with individuals aged 65 and older now representing more than 15% of the global population. This demographic shift is associated with a rising incidence of age-related cardiovascular diseases (CVDs). Early prediction and prevention of cardiovascular aging are essential to improve health outcomes among elderly patients. Objective: This study aimed to develop and externally validate a mathematical model for predicting cardiovascular aging in individuals aged 65 and older, based on general clinical and behavioral data. Methods: The model was built using data from 800 individuals aged 65+ from Almaty, Kazakhstan. Predictors included sex, marital status, education, smoking, alcohol use, disability, physical activity, total cholesterol, hypertension, BMI, coronary artery disease (CAD), myocardial infarction, diabetes mellitus, and chronic heart failure. A system of ordinary differential equations was used to simulate the dynamic interactions of these factors. Numerical integration was performed using the Runge–Kutta, Adams–Bashforth, and backward Euler methods. The model was verified statistically using Pearson correlation analysis and externally validated on independent age cohorts. In addition, we applied k-means clustering to identify hidden patterns and risk profiles within the dataset. A Random Forest classifier was trained to distinguish between high-risk and low-risk individuals using the same feature set. These machine learning approaches were used as complementary tools to enhance the robustness and interpretability of the modeling results. Results: The model trained on the 65–74 age group achieved an external validation accuracy of 98.8% and an AUC of 0.989 when applied to the 75–89 group. Risk modeling showed that in the 65–74 group, smoking and alcohol increased the risk of myocardial infarction, hypertension, and obesity by up to 53%. In the 75–89 group, these factors increased the likelihood of hypertension by 21%, chronic heart failure by 16%, and CAD by 14%. Among individuals aged 90+, hypercholesterolemia increased the risk of chronic heart failure by 17%, while hypertension increased myocardial infarction risk by 16%. Conclusions: The proposed model demonstrated high accuracy in predicting cardiovascular aging and identifying high-risk individuals across elderly subgroups. The integration of clustering and classification methods (k-means and Random Forest) provided additional insights and confirmed the consistency of the findings. This multi-method approach may serve as a valuable tool for developing personalized prevention strategies in geriatric care and improving healthy life expectancy.

Background: This study presents an innovative approach to cardiovascular disease (CVD) risk prediction based on a comprehensive analysis of clinical, immunological and biochemical markers using mathematical modelling and machine learning methods. Baseline data include indices of humoral and cellular immunity (CD59, CD16, IL-10, CD14, CD19, CD8, CD4, etc.), cytokines and markers of cardiovascular disease, inflammatory markers (TNF, GM-CSF, CRP), growth and angiogenesis factors (VEGF, PGF), proteins involved in apoptosis and cytotoxicity (perforin, CD95), as well as indices of liver function, kidney function, oxidative stress and heart failure (albumin, cystatin C, N-terminal pro B-type natriuretic peptide (NT-proBNP), superoxide dismutase (SOD), C-reactive protein (CRP), cholinesterase (ChE), cholesterol, and glomerular filtration rate (GFR)). Clinical and behavioural risk factors were also considered: arterial hypertension (AH), previous myocardial infarction (PICS), aortocoronary bypass surgery (CABG) and/or stenting, coronary heart disease (CHD), atrial fibrillation (AF), atrioventricular block (AB block), and diabetes mellitus (DM), as well as lifestyle (smoking, alcohol consumption, physical activity level), education, and body mass index (BMI). Methods: The study included 52 patients aged 65 years and older. Based on the clinical, biochemical and immunological data obtained, a model for predicting the risk of premature cardiovascular aging was developed using mathematical modelling and machine learning methods. The aim of the study was to develop a predictive model allowing for the early detection of predisposition to the development of CVDs and their complications. Numerical methods of mathematical modelling, including Runge–Kutta, Adams–Bashforth and backward-directed Euler methods, were used to solve the prediction problem, which made it possible to describe the dynamics of changes in biomarkers and patients’ condition over time with high accuracy. Results: HLA-DR (50%), CD14 (41%) and CD16 (38%) showed the highest association with aging processes. BMI was correlated with placental growth factor (37%). The glomerular filtration rate was positively associated with physical activity (47%), whereas SOD activity was negatively correlated with it (48%), reflecting a decline in antioxidant defence. Conclusions: The obtained results allow for improving the accuracy of cardiovascular risk prediction, and form personalised recommendations for the prevention and correction of its development.