A Novel Edge-Computing-Based Framework for an Intelligent Smart Healthcare System in Smart Cities

• Published in: Sustainability Vol. 15; no. 1; p. 735
• Main Authors: Tripathy, Subhranshu Sekhar, Imoize, Agbotiname Lucky, Rath, Mamata, Tripathy, Niva, Bebortta, Sujit, Lee, Cheng-Chi, Chen, Te-Yu, Ojo, Stephen, Isabona, Joseph, Pani, Subhendu Kumar
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2023
• Subjects: Artificial intelligence; Blood pressure; Cardiovascular disease; Cardiovascular diseases; Cloud computing; Communication; Disruptive innovation; Electrocardiography; Fog; Health care; Health care industry; Heart diseases; Hospitals; Infrastructure; Internet of Things; Latency; Learning algorithms; Machine learning; Medical devices; Medical electronics; Medical equipment; Medical innovations; Medical research; Medicine, Experimental; Servers; Signs and symptoms; Smart cities; Sustainability; Technology; Technology application; India
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su15010735

The wide use of internet-enabled devices has not left the healthcare sector untouched. The health status of each individual is being monitored irrespective of his/her medical conditions. The advent of such medical devices is beneficial not only for patients but also for physicians, hospitals, and insurance companies. It makes healthcare fast, reliable, and hassle-free. People can keep an eye on their blood pressure, pulse rate, etc., and thus take preventive measures on their own. In hospitals, too, the Internet of Things (IoT) is being deployed for various tasks such as monitoring oxygen and blood sugar levels, electrocardiograms (ECGs), etc. The IoT in healthcare also reduces the cost of various ailments through fast and rigorous data analysis. The prediction of diseases through machine-learning techniques based on symptoms has become a promising concept. There may also be a situation where real-time analysis is required. In such a latency-sensitive situation, fog computing plays a vital role. Establishing communication every time with the cloud is not required with the introduction of fog and thus the latency is reduced. Healthcare is a latency-sensitive application area. So, the deployment of fog computing in this area is of vital importance. Our work focuses on improving the efficiency of the system for the precise diagnosis of and recommendations for heart disease. It evaluates the system using a machine-learning module.