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      Analyzing the Patient Behavior for Improving the Medical Treatment Using Smart Healthcare and IoT-Based Deep Belief Network

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          Abstract

          Patient behavioral analysis is a critical component in treating patients with a variety of issues, with head trauma, neurological disease, and mental illness. The analysis of the patient's behavior aids in establishing the disease's core cause. Patient behavioral analysis has a number of contests that are much more problematic in traditional healthcare. With the advancement of smart healthcare, patient behavior may be simply analyzed. A new generation of information technologies, particularly the Internet of Things (IoT), is being utilized to transform the traditional healthcare system in a variety of ways. The Internet of Things (IoT) in healthcare is a crucial role in offering improved medical facilities to people as well as assisting doctors and hospitals. The proposed system comprises of a variety of medical equipment, such as mobile-based apps and sensors, which is useful in collecting and monitoring the medical information and health data of patient and interact to the doctor via network connected devices. This research may provide key information on the impact of smart healthcare and the Internet of Things in patient beavior and treatment. Patient data are exchanged via the Internet, where it is viewed and analyzed using machine learning algorithms. The deep belief neural network evaluates the patient's particulars from health data in order to determine the patient's exact health state. The developed system proved the average error rate of about 0.04 and ensured accuracy about 99% in analyzing the patient behavior.

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          Most cited references25

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          Effective Heart Disease Prediction using Hybrid Machine Learning Techniques

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            A study on medical Internet of Things and Big Data in personalized healthcare system

            Personalized healthcare systems deliver e-health services to fulfill the medical and assistive needs of the aging population. Internet of Things (IoT) is a significant advancement in the Big Data era, which supports many real-time engineering applications through enhanced services. Analytics over data streams from IoT has become a source of user data for the healthcare systems to discover new information, predict early detection, and makes decision over the critical situation for the improvement of the quality of life. In this paper, we have made a detailed study on the recent emerging technologies in the personalized healthcare systems with the focus towards cloud computing, fog computing, Big Data analytics, IoT and mobile based applications. We have analyzed the challenges in designing a better healthcare system to make early detection and diagnosis of diseases and discussed the possible solutions while providing e-health services in secure manner. This paper poses a light on the rapidly growing needs of the better healthcare systems in real-time and provides possible future work guidelines.
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              Internet-of-Things and Smart Homes for Elderly Healthcare: An End User Perspective

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                Author and article information

                Contributors
                Journal
                J Healthc Eng
                J Healthc Eng
                JHE
                Journal of Healthcare Engineering
                Hindawi
                2040-2295
                2040-2309
                2022
                10 March 2022
                : 2022
                : 6389069
                Affiliations
                1Department of Chemistry, College of Science, Jouf University, P.O. Box 2014, Sakaka, Saudi Arabia
                2Physics and Mathematics Department, Faculty of Engineering, Mataria, Helwan University, Egypt
                3Computer Studies Department, Elgraif Sharg Technological College, Sudan Technological University, Khartoum, Sudan
                4Laboratory of Nano-Smart Materials for Science and Technology (LNSMST), Department of Physics, Faculty of Science, King Khalid University, P.O. Box 9004, Abha 61413, Saudi Arabia
                5Research Center for Advanced Materials Science (RCAMS), King Khalid University, P.O. Box 9004, Abha 61413, Saudi Arabia
                6Nanoscience Laboratory for Environmental and Biomedical Applications (NLEBA), Metallurgical Lab. 1, Department of Physics, Faculty of Education, Ain Shams University, Roxy, Cairo 11757, Egypt
                7Department of Chemistry, College of Science, Taif University, P. O. Box 11099, Taif 21944, Saudi Arabia
                Author notes

                Academic Editor: Mohamed Elhoseny

                Author information
                https://orcid.org/0000-0002-0627-7989
                https://orcid.org/0000-0002-0549-5044
                https://orcid.org/0000-0002-8562-4749
                Article
                10.1155/2022/6389069
                8930207
                35310183
                236b95e2-1299-4a02-965c-c82b6e2e614d
                Copyright © 2022 Rasha M. K. Mohamed et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 24 January 2022
                : 13 February 2022
                : 22 February 2022
                Funding
                Funded by: King Khalid University
                Award ID: KKU/RCAMS/G013-21
                Funded by: Ministry of Education
                Award ID: IFP-KKU-2020/9
                Funded by: Taif University
                Award ID: TURSP-2020/91
                Categories
                Research Article

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