Big Data Analytics for Prediction Modelling in Healthcare Databases

Chauhan, R; Yafi, E

Big Data Analytics for Prediction Modelling in Healthcare Databases

Chauhan, R Yafi, E

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2021 15th International Conference on Ubiquitous Information Management and Communication (IMCOM), 2021, 00, pp. 1-5
Issue Date:: 2021-03-17

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Field	Value	Language
dc.contributor.author	Chauhan, R
dc.contributor.author	Yafi, E https://orcid.org/0000-0002-3144-0784
dc.contributor.editor	Lee, S
dc.contributor.editor	Choo, H
dc.contributor.editor	Ismail, R
dc.date	2021-01-04
dc.date.accessioned	2022-06-28T00:03:19Z
dc.date.available	2022-06-28T00:03:19Z
dc.date.issued	2021-03-17
dc.identifier.citation	2021 15th International Conference on Ubiquitous Information Management and Communication (IMCOM), 2021, 00, pp. 1-5
dc.identifier.isbn	978-1-6654-2318-2
dc.identifier.issn	2644-0164
dc.identifier.uri	http://hdl.handle.net/10453/158415
dc.description.abstract	Bigdata in healthcare has manifested as well as benefited healthcare practioners and scientists around the globe to detect hidden patterns for future clinical decision making. The major complexity faced in real world application domain is the volume of Electronic Health Records (EHR) which has gathered due to high end IT based technology which has boomed in past century for early detection of disease. The traditional technology tools adopted were incapable to discover hidden patterns due to its computational requirements. So, Big data has its generosity need in healthcare intervene technology due to diverse nature of data and accelerated speed of data that needs to processed for better diagnostic interventions. This study has been conducted using predictive data analytics on big data for discovery of knowledge for future decision making. The study consists of information about 3,56,507 patients from 1982-2010. Data curation has been done by organizing under various categories including Age, Year (1982-2010), Incidence Counts (1982-2010, all age groups and both genders), and Mortality Counts (1982-2010, all age groups). The results represents invariable patterns which can be utilized for future predictive modelling.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2021 15th International Conference on Ubiquitous Information Management and Communication (IMCOM)
dc.relation.ispartof	International Conference on Ubiquitous Information Management and Communication
dc.relation.ispartofseries	International Conference on Ubiquitous Information Management and Communication-IMCOM
dc.relation.isbasedon	10.1109/imcom51814.2021.9377403
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Big Data Analytics for Prediction Modelling in Healthcare Databases
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Seoul, Korea (South)
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 Computer Science
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-06-28T00:03:18Z
pubs.finish-date	2021-01-06
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2021-01-04
pubs.volume	00
dc.location	Piscataway, USA

Abstract:

Bigdata in healthcare has manifested as well as benefited healthcare practioners and scientists around the globe to detect hidden patterns for future clinical decision making. The major complexity faced in real world application domain is the volume of Electronic Health Records (EHR) which has gathered due to high end IT based technology which has boomed in past century for early detection of disease. The traditional technology tools adopted were incapable to discover hidden patterns due to its computational requirements. So, Big data has its generosity need in healthcare intervene technology due to diverse nature of data and accelerated speed of data that needs to processed for better diagnostic interventions. This study has been conducted using predictive data analytics on big data for discovery of knowledge for future decision making. The study consists of information about 3,56,507 patients from 1982-2010. Data curation has been done by organizing under various categories including Age, Year (1982-2010), Incidence Counts (1982-2010, all age groups and both genders), and Mortality Counts (1982-2010, all age groups). The results represents invariable patterns which can be utilized for future predictive modelling.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/158415