Trends in Using IoT with Machine Learning in Health Prediction System

Aldahiri, A; Alrashed, B; Hussain, W

Trends in Using IoT with Machine Learning in Health Prediction System

Aldahiri, A Alrashed, B Hussain, W

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Publisher:: MDPI AG
Publication Type:: Journal Article
Citation:: Forecasting, 3, (1), pp. 181-206

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Field	Value	Language
dc.contributor.author	Aldahiri, A
dc.contributor.author	Alrashed, B
dc.contributor.author	Hussain, W https://orcid.org/0000-0003-0610-4006
dc.date.accessioned	2021-03-09T23:11:08Z
dc.date.available	2021-03-09T23:11:08Z
dc.identifier.citation	Forecasting, 3, (1), pp. 181-206
dc.identifier.issn	2571-9394
dc.identifier.uri	http://hdl.handle.net/10453/146975
dc.description.abstract	<jats:p>Machine learning (ML) is a powerful tool that delivers insights hidden in Internet of Things (IoT) data. These hybrid technologies work smartly to improve the decision-making process in different areas such as education, security, business, and the healthcare industry. ML empowers the IoT to demystify hidden patterns in bulk data for optimal prediction and recommendation systems. Healthcare has embraced IoT and ML so that automated machines make medical records, predict disease diagnoses, and, most importantly, conduct real-time monitoring of patients. Individual ML algorithms perform differently on different datasets. Due to the predictive results varying, this might impact the overall results. The variation in prediction results looms large in the clinical decision-making process. Therefore, it is essential to understand the different ML algorithms used to handle IoT data in the healthcare sector. This article highlights well-known ML algorithms for classification and prediction and demonstrates how they have been used in the healthcare sector. The aim of this paper is to present a comprehensive overview of existing ML approaches and their application in IoT medical data. In a thorough analysis, we observe that different ML prediction algorithms have various shortcomings. Depending on the type of IoT dataset, we need to choose an optimal method to predict critical healthcare data. The paper also provides some examples of IoT and machine learning to predict future healthcare system trends.</jats:p>
dc.language	en
dc.publisher	MDPI AG
dc.relation.ispartof	Forecasting
dc.relation.isbasedon	10.3390/forecast3010012
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Trends in Using IoT with Machine Learning in Health Prediction System
dc.type	Journal Article
utslib.citation.volume	3
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
utslib.copyright.status	open_access	*
dc.date.updated	2021-03-09T23:10:44Z
pubs.issue	1
pubs.publication-status	Published online
pubs.volume	3
utslib.citation.issue	1

Abstract:

Machine learning (ML) is a powerful tool that delivers insights hidden in Internet of Things (IoT) data. These hybrid technologies work smartly to improve the decision-making process in different areas such as education, security, business, and the healthcare industry. ML empowers the IoT to demystify hidden patterns in bulk data for optimal prediction and recommendation systems. Healthcare has embraced IoT and ML so that automated machines make medical records, predict disease diagnoses, and, most importantly, conduct real-time monitoring of patients. Individual ML algorithms perform differently on different datasets. Due to the predictive results varying, this might impact the overall results. The variation in prediction results looms large in the clinical decision-making process. Therefore, it is essential to understand the different ML algorithms used to handle IoT data in the healthcare sector. This article highlights well-known ML algorithms for classification and prediction and demonstrates how they have been used in the healthcare sector. The aim of this paper is to present a comprehensive overview of existing ML approaches and their application in IoT medical data. In a thorough analysis, we observe that different ML prediction algorithms have various shortcomings. Depending on the type of IoT dataset, we need to choose an optimal method to predict critical healthcare data. The paper also provides some examples of IoT and machine learning to predict future healthcare system trends.

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http://hdl.handle.net/10453/146975