Monitoring Acute Heart Failure Patients Using Internet-of-Things-Based Smart Monitoring System

Area of Science:

• Biomedical Engineering
• Computer Science
• Artificial Intelligence

Background:

• The COVID-19 pandemic accelerated the shift towards online medical services and smart health monitoring systems.
• Technological advancements, particularly the Internet of Things (IoT) and Artificial Intelligence (AI), are transforming healthcare delivery.
• Smart homes and smart medical systems are increasingly popular, necessitating advanced data management and decision-making capabilities.

Purpose of the Study:

• To design and develop an integrated health monitoring system for cardiac patients.
• To leverage AI and IoT for real-time patient activity tracking and health status updates.
• To implement machine learning models for accurate disease classification in heart patients.

Main Methods:

• Development of a health monitoring system integrating Internet of Things (IoT) sensors for data acquisition.
• Application of Artificial Intelligence (AI) algorithms for processing and managing large volumes of patient data.
• Implementation of machine learning models for real-time disease classification and patient monitoring.

Main Results:

• The proposed system enables real-time monitoring of heart patients' activities and health status.
• Experimental results demonstrate the system's capability for accurate disease classification.
• The AI and IoT integration facilitates efficient data handling and informed decision-making for patient care.

Conclusions:

• The AI and IoT-based system offers a robust solution for smart health monitoring of cardiac patients.
• The system enhances patient engagement by providing timely health status information.
• The accurate disease classification capabilities support early diagnosis and improved patient outcomes.