Heart failure patients monitoring using IoT-based remote monitoring system

被引:6
|
作者
Umer, Muhammad [1 ]
Aljrees, Turki [2 ]
Karamti, Hanen [3 ]
Ishaq, Abid [1 ]
Alsubai, Shtwai [4 ]
Omar, Marwan [5 ]
Bashir, Ali Kashif [6 ,7 ,8 ]
Ashraf, Imran [9 ]
机构
[1] Islamia Univ Bahawalpur, Dept Comp Sci & Informat Technol, Bahawalpur 63100, Pakistan
[2] Univ Hafr Al Batin, Dept Coll Comp Sci & Engn, Hafar Al Batin 39524, Saudi Arabia
[3] Princess Nourah Bint Abdulrahman Univ, Coll Comp & Informat Sci, Dept Comp Sci, POB 84428, Riyadh 11671, Saudi Arabia
[4] Prince Sattam Bin Abdulaziz Univ, Coll Comp Engn & Sci, Dept Comp Sci, POB 151, Al Kharj 11942, Saudi Arabia
[5] IIT, Informat Technol & Management, Chicago, IL USA
[6] Manchester Metropolitan Univ, Dept Comp & Math, Manchester, Lancs, England
[7] Woxsen Univ, Woxsen Sch Business, Hyderabad 502345, India
[8] Lebanese Amer Univ, Dept Comp Sci & Math, Beirut, Lebanon
[9] Yeungnam Univ, Informat & Commun Engn, Gyongsan 38541, South Korea
关键词
HEALTH-CARE-SYSTEM; INTERNET; PREDICTION; SERVICE; LEVEL; IOMT;
D O I
10.1038/s41598-023-46322-6
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
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.
引用
收藏
页数:19
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