Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Book Chapter Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Edge computing for smart disease prediction treatment therapy Subject The topic of the resource Aautomated medical diagnosis systems; Cardio-vascular diseases; Deep learning; Edge computing; Healthcare system; Machine learning; Sensors; Smart devices; Surveillance frameworks Description An account of the resource Healthcare systems are increasingly seeking to match patients' pace of life and be personalized, as they are demanding more advanced products and services. The only solution for collecting and analyzing health data in realtime is an edge computing (EC) environment, coupled with 5G speeds and modern computing techniques. The technology in healthcare is currently being used to develop smart systems that can expedite the diagnosis of disease and provide precise and timely treatment. The automated hospital monitoring system and medical diagnosis system enable doctors to monitor and diagnose patients from a variety of locations, including hospitals, workplaces, and homes and provide transportation options. As a result, overall doctor visits are reduced as well as patient care is improved. More than 162 billion healthcare IoT devices are expected to be used worldwide by 2021 thanks to the internet of things (IoT) sensors and applications for general healthcare. With edge intelligence (EI), wearable devices with sensors, like smartwatches or smartphones, and gateway devices, such as microcontrollers, can form edge nodes: smart devices with sensors, as well as gateway devices with sensors, can act as edge nodes. Smart sensor devices are typically installed at a greater distance from personal computers (PCs) and servers, which can be utilized in fog computing (FC). In healthcare, EC and FC are used to deliver reliable, low-latency, and location-aware healthcare services by utilizing sensors located within users' reach. Recently, many researchers have proposed using hierarchical computing for the distribution and allocation of inference-based tasks among edge devices and fog nodes, which could lead to an increase in computing power and compute capability of edge devices. For disease prediction, this chapter discusses a variety of EC techniques. 2024 Apple Academic Press, Inc. All rights reserved. Creator An entity primarily responsible for making the resource Nanjundan P.; Jaisingh W.; George J.P. Source A related resource from which the described resource is derived Reconnoitering the Landscape of Edge Intelligence in Healthcare, pp. 89-114. Publisher An entity responsible for making the resource available Apple Academic Press Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="" target="_blank" rel="noreferrer noopener"></a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185094359&amp;partnerID=40&amp;md5=21c020c5c7134f5c43794d07b3ef1726" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185094359&amp;partnerID=40&amp;md5=21c020c5c7134f5c43794d07b3ef1726</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-100089493-6; 978-177491436-6 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Book chapter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Nanjundan P., Department of Data Science, Christ University, Lavasa, Pune, Maharashtra, India; Jaisingh W., School of Computing Science and Engineering, VIT Bhopal University, Bhopal, Madhya Pradesh, India; George J.P., Department of Data Science, Christ University, Lavasa, Pune, Maharashtra, India