Vascular Vigilance: an IoT-Integrated Deep Learning Approach for Cardiovascular Disease Prediction and Risk Management

EasyChair Preprint no. 12952

Abstract

Vascular Vigilance: An IoT-Integrated Deep Learning Approach for Cardiovascular Disease Prediction and Risk Management introduces a novel framework merging Internet of Things (IoT) technology and deep learning to revolutionize cardiovascular health care. Traditional methods often lack the ability to capture subtle risk factors, leading to delayed interventions and poorer outcomes. Vascular Vigilance addresses this gap by utilizing IoT devices for continuous data collection and deep learning algorithms for predictive analysis. By monitoring real-time physiological parameters and lifestyle behaviors, and leveraging advanced analytics to discern patterns, Vascular Vigilance offers personalized risk assessments and actionable recommendations. This approach has the potential to empower clinicians and patients alike, fostering proactive management of cardiovascular risk and ultimately improving patient outcomes.

Keyphrases: Cardiovascular, Disease, prediction

@Booklet{EasyChair:12952,
  author = {Jane Smith and Chris Liu},
  title = {Vascular Vigilance: an IoT-Integrated Deep Learning Approach for Cardiovascular Disease Prediction and Risk Management},
  howpublished = {EasyChair Preprint no. 12952},

  year = {EasyChair, 2024}}