Department of Chemistry, College of Science, Jouf University, P.O. Box 2014, Sakaka, Saudi Arabia.
Physics and Mathematics Department, Faculty of Engineering, Mataria, Helwan University, Egypt.
J Healthc Eng. 2022 Mar 10;2022:6389069. doi: 10.1155/2022/6389069. eCollection 2022.
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.
患者行为分析是治疗各种问题患者的重要组成部分，包括头部创伤、神经疾病和精神疾病。对患者行为的分析有助于确定疾病的核心原因。患者行为分析存在一些挑战，在传统医疗保健中，这些挑战更为突出。随着智能医疗保健的进步，患者行为可能会被简单地分析。新一代信息技术，特别是物联网（IoT），正在以多种方式改变传统的医疗保健系统。物联网（IoT）在医疗保健中扮演着重要角色，它为人们提供了更好的医疗设施，同时也为医生和医院提供了帮助。该系统包括各种医疗设备，如基于移动的应用程序和传感器，这些设备可用于收集和监测患者的医疗信息和健康数据，并通过网络连接的设备与医生进行交互。这项研究可以为智能医疗保健和物联网对患者行为和治疗的影响提供关键信息。患者数据通过互联网进行交换，在那里使用机器学习算法对其进行查看和分析。深度置信神经网络从健康数据中评估患者的具体情况，以确定患者的确切健康状况。所开发的系统在分析患者行为时，其平均错误率约为 0.04，准确率约为 99%。
