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

Author:

Almujally Nouf Abdullah1ORCID,Aljrees Turki2ORCID,Saidani Oumaima1ORCID,Umer Muhammad3ORCID,Faheem Zaid Bin3ORCID,Abuzinadah Nihal4ORCID,Alnowaiser Khaled5,Ashraf Imran6ORCID

Affiliation:

1. Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia

2. College of Computer Science and Engineering, University of Hafr Al-Batin, Hafar Al-Batin 39524, Saudi Arabia

3. Department of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan

4. Faculty of Computer Science and Information Technology, King Abdulaziz University, P.O. Box 80200, Jeddah 21589, Saudi Arabia

5. Department of Computer Engineering, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia

6. Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea

Abstract

With technological advancements, smart health monitoring systems are gaining growing importance and popularity. Today, business trends are changing from physical infrastructure to online services. With the restrictions imposed during COVID-19, medical services have been changed. The concepts of smart homes, smart appliances, and smart medical systems have gained popularity. The Internet of Things (IoT) has revolutionized communication and data collection by incorporating smart sensors for data collection from diverse sources. In addition, it utilizes artificial intelligence (AI) approaches to control a large volume of data for better use, storing, managing, and making decisions. In this research, a health monitoring system based on AI and IoT is designed to deal with the data of heart patients. The system monitors the heart patient’s activities, which helps to inform patients about their health status. Moreover, the system can perform disease classification using machine learning models. Experimental results reveal that the proposed system can perform real-time monitoring of patients and classify diseases with higher accuracy.

Funder

Princess Nourah bint Abdulrahman University

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

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