Predictive Wellness: DEEP-CARDIO Framework for Personalized Cardiovascular Risk Management

EasyChair Preprint 12947

Abstract

Predictive Wellness introduces the DEEP-CARDIO Framework, a pioneering initiative aimed at personalized cardiovascular risk management through predictive analytics. With cardiovascular diseases (CVDs) remaining a leading cause of morbidity and mortality globally, there is an urgent need for innovative approaches to preventive care. The DEEP-CARDIO Framework leverages advanced data analytics and machine learning algorithms to analyze real-time physiological data obtained from Internet of Things (IoT) devices. This framework enables continuous monitoring of key cardiovascular parameters, including heart rate variability, blood pressure trends, physical activity levels, and sleep patterns, providing a comprehensive assessment of an individual's cardiovascular health status. Central to the DEEP-CARDIO Framework is its predictive modeling capabilities, which utilize deep learning algorithms to identify subtle patterns and correlations indicative of potential cardiovascular risk factors. By continuously learning from incoming data streams, the framework refines its predictive models over time, enhancing the accuracy and reliability of risk assessments. Through personalized risk stratification, the DEEP-CARDIO Framework empowers individuals to take proactive measures towards optimizing their cardiovascular health, offering tailored recommendations for lifestyle modifications and interventions Overall, the DEEP-CARDIO Framework represents a transformative approach to preventive cardiology, offering personalized risk management strategies to improve cardiovascular health outcomes.

Keyphrases: Cardiovascular, Risk, management

@booklet{EasyChair:12947,
  author    = {Kurez Oroy and Emily Carter},
  title     = {Predictive Wellness: DEEP-CARDIO Framework for Personalized Cardiovascular Risk Management},
  howpublished = {EasyChair Preprint 12947},
  year      = {EasyChair, 2024}}