A smart IoMT based architecture for E-healthcare patient monitoring system using artificial intelligence algorithms

Generally, cloud computing is integrated with wireless sensor network to enable the monitoring systems and it improves the quality of service. The sensed patient data are monitored with biosensors without considering the patient datatype and this minimizes the work of hospitals and physicians. Weara...

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Bibliographic Details
Published in:	Frontiers in physiology Vol. 14; p. 1125952
Main Authors:	A, Ahila, Dahan, Fadl, Alroobaea, Roobaea, Alghamdi, Wael Y, Mustafa Khaja Mohammed, Hajjej, Fahima, Deema Mohammed Alsekait, Raahemifar, Kaamran
Format:	Journal Article
Language:	English
Published:	Switzerland Frontiers Media S.A 30-01-2023
Subjects:	cloud computing healthcare data high dimensional LDA hybrid ResNet 18 and GoogLeNet classifier internet of medical things (IoMT) multi-objective CSA Physiology healthcare data hybrid ResNet 18 and GoogLeNet classifier internet of medical things (IoMT) high dimensional LDA cloud computing multi-objective CSA sensed device
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Summary:	Generally, cloud computing is integrated with wireless sensor network to enable the monitoring systems and it improves the quality of service. The sensed patient data are monitored with biosensors without considering the patient datatype and this minimizes the work of hospitals and physicians. Wearable sensor devices and the Internet of Medical Things (IoMT) have changed the health service, resulting in faster monitoring, prediction, diagnosis, and treatment. Nevertheless, there have been difficulties that need to be resolved by the use of AI methods. The primary goal of this study is to introduce an AI-powered, IoMT telemedicine infrastructure for E-healthcare. In this paper, initially the data collection from the patient body is made using the sensed devices and the information are transmitted through the gateway/Wi-Fi and is stored in IoMT cloud repository. The stored information is then acquired, preprocessed to refine the collected data. The features from preprocessed data are extracted by means of high dimensional Linear Discriminant analysis (LDA) and the best optimal features are selected using reconfigured multi-objective cuckoo search algorithm (CSA). The prediction of abnormal/normal data is made by using Hybrid ResNet 18 and GoogleNet classifier (HRGC). The decision is then made whether to send alert to hospitals/healthcare personnel or not. If the expected results are satisfactory, the participant information is saved in the internet for later use. At last, the performance analysis is carried so as to validate the efficiency of proposed mechanism.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Edited by: Poongodi M., Hamad bin Khalifa University, Qatar Reviewed by: Merline Arulraj, Sethu Institute of Technology (SIT), India Sujatha C., SSM Institute of Engineering and Technology, India This article was submitted to Computational Physiology and Medicine, a section of the journal Frontiers in Physiology
ISSN:	1664-042X 1664-042X
DOI:	10.3389/fphys.2023.1125952