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. 2022 Mar 10:2022:6389069.
doi: 10.1155/2022/6389069. eCollection 2022.

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

Rasha M K Mohamed  1 , Osama R Shahin  2 , Nadir O Hamed  3 , Heba Y Zahran  4   5   6 , Magda H Abdellattif  7
Affiliations

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

Rasha M K Mohamed et al. J Healthc Eng. .

Retraction in

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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Conflict of interest statement

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Figures

Figure 1
Figure 1
Block diagram of the IoT-based smart technology.
Figure 2
Figure 2
IoT-based data processing unit.
Figure 3
Figure 3
Deep belief neural network for IoT smart health monitoring.
Figure 4
Figure 4
Deviation in room humidity.
Figure 5
Figure 5
Error rate of the developed system.
Figure 6
Figure 6
Data collected for room humidity.
Figure 7
Figure 7
Data observed using the IoT smart health monitoring system.
Figure 8
Figure 8
Error rate prediction using deep belief neural network.
Figure 9
Figure 9
Validation of Precision rate.
Figure 10
Figure 10
Accuracy rate.
See this image and copyright information in PMC

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