Hash polynomial two factor decision tree using IoT for smart health care scheduling

Manikandan, R; Patan, R; Gandomi, AH; Sivanesan, P; Kalyanaraman, H

Hash polynomial two factor decision tree using IoT for smart health care scheduling

Manikandan, R Patan, R Gandomi, AH Sivanesan, P Kalyanaraman, H

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Publisher:: PERGAMON-ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Expert Systems with Applications, 2020, 141
Issue Date:: 2020-03-01

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Field	Value	Language
dc.contributor.author	Manikandan, R
dc.contributor.author	Patan, R
dc.contributor.author	Gandomi, AH
dc.contributor.author	Sivanesan, P
dc.contributor.author	Kalyanaraman, H
dc.date.accessioned	2021-01-14T21:52:48Z
dc.date.available	2021-01-14T21:52:48Z
dc.date.issued	2020-03-01
dc.identifier.citation	Expert Systems with Applications, 2020, 141
dc.identifier.issn	0957-4174
dc.identifier.issn	1873-6793
dc.identifier.uri	http://hdl.handle.net/10453/145444
dc.description.abstract	© 2019 The steady growth of an aging population and increased frequency of chronic disease led to the development of Smart Health Care (SHC) systems. While patient prioritization is the core of any SHC system, handling the response time by medical practitioners is a prevailing challenge. With advancements in information technology, the concept of the Internet of Things (IoT) has made it possible to integrate SHC systems with the Cloud environment to not only ensure patient prioritization according to disease prevalence, but also to minimize response time. In this work, an IoT-based scheduling method, called the Hash Polynomial Two-factor Decision Tree (HP-TDT) is proposed to increase scheduling efficiency and reduce response time by classifying patients as being normal or in a critical state in minimal time. The HP-TDT scheduling method involves three stages including the registration stage, the data collection stage, and the scheduling stage. The registration phase is carried out through Open Address Hashing (OAH) model for reducing the key generation response time. Next, the data collection stage is performed using the Polynomial Data Collection (PDC) algorithm. By incorporating PDC, computation overhead is reduced because a number of operations are considered during data collection. Finally, scheduling is performed by applying two-factor, entropy and information gain according to a decision tree. With this, scheduling efficiency is improved due to the classification of patients as being normal or in a critical state. The proposed method minimizes response time, computational overhead, and improves essential scheduling efficiency.
dc.language	English
dc.publisher	PERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartof	Expert Systems with Applications
dc.relation.isbasedon	10.1016/j.eswa.2019.112924
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	01 Mathematical Sciences, 08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Hash polynomial two factor decision tree using IoT for smart health care scheduling
dc.type	Journal Article
utslib.citation.volume	141
utslib.for	01 Mathematical Sciences
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
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/A/DRsch The Data Science Institute
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2022-03-01T00:00:00+1000Z
dc.date.updated	2021-01-14T21:52:37Z
pubs.publication-status	Published
pubs.volume	141

Abstract:

© 2019 The steady growth of an aging population and increased frequency of chronic disease led to the development of Smart Health Care (SHC) systems. While patient prioritization is the core of any SHC system, handling the response time by medical practitioners is a prevailing challenge. With advancements in information technology, the concept of the Internet of Things (IoT) has made it possible to integrate SHC systems with the Cloud environment to not only ensure patient prioritization according to disease prevalence, but also to minimize response time. In this work, an IoT-based scheduling method, called the Hash Polynomial Two-factor Decision Tree (HP-TDT) is proposed to increase scheduling efficiency and reduce response time by classifying patients as being normal or in a critical state in minimal time. The HP-TDT scheduling method involves three stages including the registration stage, the data collection stage, and the scheduling stage. The registration phase is carried out through Open Address Hashing (OAH) model for reducing the key generation response time. Next, the data collection stage is performed using the Polynomial Data Collection (PDC) algorithm. By incorporating PDC, computation overhead is reduced because a number of operations are considered during data collection. Finally, scheduling is performed by applying two-factor, entropy and information gain according to a decision tree. With this, scheduling efficiency is improved due to the classification of patients as being normal or in a critical state. The proposed method minimizes response time, computational overhead, and improves essential scheduling efficiency.

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http://hdl.handle.net/10453/145444