Heart failure patients monitoring using IoT-based remote monitoring system

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作者
Muhammad Umer
Turki Aljrees
Hanen Karamti
Abid Ishaq
Shtwai Alsubai
Marwan Omar
Ali Kashif Bashir
Imran Ashraf
机构
[1] The Islamia University of Bahawalpur,Department of Computer Science and Information Technology
[2] University of Hafr Al-Batin,Department College of Computer Science and Engineering
[3] Princess Nourah bint Abdulrahman University,Department of Computer Sciences, College of Computer and Information Sciences
[4] Prince Sattam bin Abdulaziz University,Department of Computer Science, College of Computer Engineering and Sciences
[5] Illinois Institute of Technology,Information Technology and Management
[6] Manchester Metropolitan University,Department of Computing and Mathematics
[7] Woxsen University,Woxsen School of Business
[8] Lebanese American University,Department of Computer Science and Mathematics
[9] Yeungnam University,Information and Communication Engineering
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摘要
Intelligent health monitoring systems are becoming more important and popular as technology advances. Nowadays, online services are replacing physical infrastructure in several domains including medical services as well. The COVID-19 pandemic has also changed the way medical services are delivered. Intelligent appliances, smart homes, and smart medical systems are some of the emerging concepts. The Internet of Things (IoT) has changed the way communication occurs alongside data collection sources aided by smart sensors. It also has deployed artificial intelligence (AI) methods for better decision-making provided by efficient data collection, storage, retrieval, and data management. This research employs health monitoring systems for heart patients using IoT and AI-based solutions. Activities of heart patients are monitored and reported using the IoT system. For heart disease prediction, an ensemble model ET-CNN is presented which provides an accuracy score of 0.9524. The investigative data related to this system is very encouraging in real-time reporting and classifying heart patients with great accuracy.
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