IoT Vigilance: Deep Learning for Proactive Cardiovascular Disease Management

EasyChair Preprint 12946

Abstract

IoT Vigilance presents a proactive approach to cardiovascular disease management through the integration of deep learning techniques with Internet of Things (IoT) technology. Cardiovascular diseases (CVDs) remain a leading cause of mortality globally, necessitating innovative strategies for early detection and intervention. IoT Vigilance leverages the ubiquity of IoT devices to continuously monitor physiological parameters relevant to cardiovascular health, such as heart rate variability, blood pressure trends, and physical activity levels. By analyzing this real-time data using deep learning algorithms, the system can identify subtle patterns and anomalies indicative of potential cardiovascular risks. The fusion of IoT technology with deep learning enables IoT Vigilance to provide personalized risk assessments and intervention recommendations tailored to individual profiles. By harnessing the power of predictive analytics, the system can anticipate and mitigate cardiovascular risks before they escalate into critical health issues. Moreover, IoT Vigilance facilitates seamless communication between individuals and healthcare providers, fostering a collaborative approach to proactive cardiovascular health management. This abstract presents an overview of the IoT Vigilance framework, highlighting its potential to revolutionize preventive cardiology and improve health outcomes for individuals at risk of CVDs.

Keyphrases: Cardiovascular, Disease, management

@booklet{EasyChair:12946,
  author    = {Bruse Nick and Julia Anderson},
  title     = {IoT Vigilance: Deep Learning for Proactive Cardiovascular Disease Management},
  howpublished = {EasyChair Preprint 12946},
  year      = {EasyChair, 2024}}