Integrated Health Application for Cardiovascular Disease Prediction

Cardiovascular diseases are among the major causes of death worldwide and require early detection and intervention. Advanced data analytics and AI techniques have transformed the current paradigm in cardiovascular disease prediction by marrying the health applications with intelligent forms of prediction. This survey paper provides an overview of integrated health applications designed to predict CVD, discussing their architectures, methodologies, and clinical relevance. It looks at key technologies like machine learning, deep learning, and wearable devices, always underlining their roles within risk assessment, diagnosis, and tailoring treatment. In this respect, the paper tackles issues like data privacy, interoperability, and the requirement for near-real-time analytics. This survey aspires to guide future developments in this direction by consolidating the recent advancements and identifying research gaps toward creating efficient, scalable, and user-centric cardiovascular health solutions.