Applicability of classifier to discovery knowledge for future prediction modelling

Chauhan, R; Yafi, E

Applicability of classifier to discovery knowledge for future prediction modelling

Chauhan, R Yafi, E

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Publisher:: SPRINGER HEIDELBERG
Publication Type:: Journal Article
Citation:: Journal of Ambient Intelligence and Humanized Computing, 2022
Issue Date:: 2022-01-01

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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.date.accessioned	2023-04-03T02:22:03Z
dc.date.available	2023-04-03T02:22:03Z
dc.date.issued	2022-01-01
dc.identifier.citation	Journal of Ambient Intelligence and Humanized Computing, 2022
dc.identifier.issn	1868-5137
dc.identifier.issn	1868-5145
dc.identifier.uri	http://hdl.handle.net/10453/169060
dc.description.abstract	The immense growth of new technological interventions has forced researchers and scientists around the globe to adopt the widely anticipated technology of Machine Learning (ML) and Artificial Intelligence (AI). ML and AI have generously prospected itself from the past decade in the discovery of knowledge from databases. Several ML and AI based adoptive technologies have emerged in varied application domains, and are thus widely opening a new era of knowledge in decision making. Moreover, ML and AI are techniques that can improve the treatment and diagnosis of diseases. In the current study, we have designed and deployed a “PROCLAVE”. The tool was designed in varied layers of structure, where each layer plays a significant role in determining the patterns. We have applied several libraries for the processing of a prototype to develop a visualization interface. The tool forecasts health vulnerability, makes a comparison among variable classifiers and visualize the results for end users. Moreover, the proposed architecture is based on the concepts of conceptualization and visualization to detect the overall dashboard. Furthermore, the current approach was synthesized and populated with a database that allows the end users to select the variable features and relatively determine the interactive patterns for the number of cases. The database was collected from the National Institute of Health Stroke (NIHS) in the United States. Data was gathered for stroke patients who were diagnosed with stroke from 1950 to 2015. The study was based on several attributes which included causes of death, sex, race, Hispanic origin and others to discover unknown patterns for future decision making.
dc.language	English
dc.publisher	SPRINGER HEIDELBERG
dc.relation.ispartof	Journal of Ambient Intelligence and Humanized Computing
dc.relation.isbasedon	10.1007/s12652-022-03694-3
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0805 Distributed Computing
dc.title	Applicability of classifier to discovery knowledge for future prediction modelling
dc.type	Journal Article
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0805 Distributed Computing
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	open_access	*
dc.date.updated	2023-04-03T02:22:01Z
pubs.publication-status	Published

Abstract:

The immense growth of new technological interventions has forced researchers and scientists around the globe to adopt the widely anticipated technology of Machine Learning (ML) and Artificial Intelligence (AI). ML and AI have generously prospected itself from the past decade in the discovery of knowledge from databases. Several ML and AI based adoptive technologies have emerged in varied application domains, and are thus widely opening a new era of knowledge in decision making. Moreover, ML and AI are techniques that can improve the treatment and diagnosis of diseases. In the current study, we have designed and deployed a “PROCLAVE”. The tool was designed in varied layers of structure, where each layer plays a significant role in determining the patterns. We have applied several libraries for the processing of a prototype to develop a visualization interface. The tool forecasts health vulnerability, makes a comparison among variable classifiers and visualize the results for end users. Moreover, the proposed architecture is based on the concepts of conceptualization and visualization to detect the overall dashboard. Furthermore, the current approach was synthesized and populated with a database that allows the end users to select the variable features and relatively determine the interactive patterns for the number of cases. The database was collected from the National Institute of Health Stroke (NIHS) in the United States. Data was gathered for stroke patients who were diagnosed with stroke from 1950 to 2015. The study was based on several attributes which included causes of death, sex, race, Hispanic origin and others to discover unknown patterns for future decision making.

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http://hdl.handle.net/10453/169060