Dynamic Risk Stratification for Proactive Cardiovascular Disease Management via Federated Digital Twin Networks

**Abstract:** This paper introduces a novel framework for proactive cardiovascular disease (CVD) management utilizing a federated network of digital twins. We leverage multi-modal patient data, integrated within a distributed architecture, to achieve dynamic risk stratification and personalized intervention recommendations. Unlike traditional risk assessment methods, our approach incorporates continuous bio-signal monitoring, genomic data, and lifestyle factors, processed through a sophisticated hyperparameter-tuned machine learning pipeline. This enables early detection of subclinical CVD markers, facilitating preventative interventions and ultimately reducing mortality and morbidity rates. The system boasts a projected 30% improvement in CVD event prediction compared to current models within five years and a potential \$15 billion market opportunity driven by personalized preventative care. --- *This document is a part of the Freederia Research Archive. Explore our complete collection of advanced research at [en.freederia.com](https://en.freederia.com), or visit our main portal at [freederia.com](https://freederia.com) to learn more about our mission and other initiatives.*