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Title: A Review on the Role of Machine Learning in Enabling IoT Based Healthcare Applications
Authors: Bharadwaj, Hemantha Krishna
Agarwal, Aayush
Chamola, Vinay
Lakkaniga, Naga Rajiv
Hassija, Vikas
Guizani, Mohsen
Sikdar, Biplab 
Keywords: cardiovascular
Internet of Things
machine learning
Issue Date: 1-Jan-2021
Publisher: Institute of Electrical and Electronics Engineers Inc.
Citation: Bharadwaj, Hemantha Krishna, Agarwal, Aayush, Chamola, Vinay, Lakkaniga, Naga Rajiv, Hassija, Vikas, Guizani, Mohsen, Sikdar, Biplab (2021-01-01). A Review on the Role of Machine Learning in Enabling IoT Based Healthcare Applications. IEEE Access 9 : 38859-38890. ScholarBank@NUS Repository.
Rights: Attribution 4.0 International
Abstract: The Internet of Things (IoT) is playing a vital role in the rapid automation of the healthcare sector. The branch of IoT dedicated towards medical science is at times termed as Healthcare Internet of Things (H-IoT). The key elements of all H-IoT applications are data gathering and processing. Due to the large amount of data involved in healthcare, and the enormous value that accurate predictions hold, the integration of machine learning (ML) algorithms into H-IoT is imperative. This paper aims to serve both as a compilation as well as a review of the various state of the art applications of ML algorithms currently being integrated with H-IoT. Some of the most widely used ML algorithms have been briefly introduced and their use in various H-IoT applications has been analyzed in terms of their advantages, scope, and possible improvements. Applications have been divided into the domains of diagnosis, prognosis and spread control, assistive systems, monitoring, and logistics. In healthcare, practical use of a model requires it to be highly accurate and to have ample measures against security attacks. The applications of ML algorithms in H-IoT discussed in this paper have shown experimental evidence of accuracy and practical usability. The constraints and drawbacks of each of these applications have also been described. © 2013 IEEE.
Source Title: IEEE Access
ISSN: 2169-3536
DOI: 10.1109/access.2021.3059858
Rights: Attribution 4.0 International
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