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Prediction of hidden knowledge from Clinical Database using data mining techniques

Clinical Database has enormous quantity of information about patients and their diseases. The database mainly contains clinical consultation details, family history, medical lab report and other information which are considered to taking a final diagnostic decision by physician. Clinical databases a...

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Main Authors: Thangarasu, G., Dominic, P.D.D.
Format: Conference or Workshop Item
Published: Institute of Electrical and Electronics Engineers Inc. 2014
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938790845&doi=10.1109%2fICCOINS.2014.6868414&partnerID=40&md5=15d3a467507e96430feedde1d43f0b81
http://eprints.utp.edu.my/31215/
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spelling my.utp.eprints.312152022-03-25T09:03:16Z Prediction of hidden knowledge from Clinical Database using data mining techniques Thangarasu, G. Dominic, P.D.D. Clinical Database has enormous quantity of information about patients and their diseases. The database mainly contains clinical consultation details, family history, medical lab report and other information which are considered to taking a final diagnostic decision by physician. Clinical databases are widely utilized by the numerous researchers for predicting different diseases. The current diabetes diagnosis methods are carried out based on the impact of various medical test and the results of physical examination. The new and innovative prediction methods are projected in this research to identify the diabetic disease, its types and complications from the clinical database in an efficiently and an economically faster manner. © 2014 IEEE. Institute of Electrical and Electronics Engineers Inc. 2014 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938790845&doi=10.1109%2fICCOINS.2014.6868414&partnerID=40&md5=15d3a467507e96430feedde1d43f0b81 Thangarasu, G. and Dominic, P.D.D. (2014) Prediction of hidden knowledge from Clinical Database using data mining techniques. In: UNSPECIFIED. http://eprints.utp.edu.my/31215/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description Clinical Database has enormous quantity of information about patients and their diseases. The database mainly contains clinical consultation details, family history, medical lab report and other information which are considered to taking a final diagnostic decision by physician. Clinical databases are widely utilized by the numerous researchers for predicting different diseases. The current diabetes diagnosis methods are carried out based on the impact of various medical test and the results of physical examination. The new and innovative prediction methods are projected in this research to identify the diabetic disease, its types and complications from the clinical database in an efficiently and an economically faster manner. © 2014 IEEE.
format Conference or Workshop Item
author Thangarasu, G.
Dominic, P.D.D.
spellingShingle Thangarasu, G.
Dominic, P.D.D.
Prediction of hidden knowledge from Clinical Database using data mining techniques
author_facet Thangarasu, G.
Dominic, P.D.D.
author_sort Thangarasu, G.
title Prediction of hidden knowledge from Clinical Database using data mining techniques
title_short Prediction of hidden knowledge from Clinical Database using data mining techniques
title_full Prediction of hidden knowledge from Clinical Database using data mining techniques
title_fullStr Prediction of hidden knowledge from Clinical Database using data mining techniques
title_full_unstemmed Prediction of hidden knowledge from Clinical Database using data mining techniques
title_sort prediction of hidden knowledge from clinical database using data mining techniques
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2014
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938790845&doi=10.1109%2fICCOINS.2014.6868414&partnerID=40&md5=15d3a467507e96430feedde1d43f0b81
http://eprints.utp.edu.my/31215/
_version_ 1738657216537296896
score 13.160551

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