Advanced Cardiovascular Health Management Through Comprehensive Risk Assessment and Predictive Modeling of Cardiac Arrest

Cardiovascular diseases, with cardiac arrest being a prominent example, continue to be a leading global health concern. In this context, effective risk assessment and predictive modeling play a pivotal role in enhancing early diagnosis and tailored management of cardiac arrest. This research paper presents an extensive investigation into the comprehensive risk assessment and predictive modeling of cardiac arrest, incorporating clinical and demographic features as essential components for a holistic approach to cardiovascular health management. Our study focuses on a set of 13 key features. These features are commonly recognized in cardiology as influential factors in cardiac arrest diagnosis and prognosis. Utilizing new-fangled machine learning techniques, we construct predictive models that offer risk assessments for cardiac arrest, enabling early identification of individuals at risk of a heart attack or other cardiovascular events. The critical role of these models in this healthcare context, facilitating improved patient care and tailored interventions for individuals susceptible to cardiac arrest. The research culminates in a multifaceted approach to cardiac arrest risk assessment and predictive modeling, harnessing the synergy of clinical and demographic data. Our findings underscore the significance of early intervention and individualized care, further advancing the goal of reducing the global burden of cardiovascular diseases, particularly in the context of cardiac arrest.