Synthetic data integration in cardiovascular research: enhancing analysis accuracy and data privacy with EPICOSAI

Abstract Introduction Cardiovascular research faces challenges such as limited patient data, privacy concerns, and analytical complexity. EPICOSAI addresses these by generating high-fidelity synthetic data, enabling robust analysis without compromising confidentiality. Methods Real cardiovascular datasets were used to simulate synthetic patient data via Monte Carlo simulations. EPICOSAI applied normal, binomial, Bernoulli, and exponential distributions to model variables like age, ejection fraction, comorbidities, and time-to-event outcomes. Generated datasets were validated through descriptive statistics, distribution matching, and machine learning model comparisons. Results Synthetic datasets showed