Applicability of data mining algoritms on clinical datasets

dc.contributor.author	Bonney, Wilfred	en
dc.date.accessioned	2015-04-27T14:40:57Z
dc.date.available	2015-04-27T14:40:57Z
dc.date.issued	2015-04-27
dc.identifier.uri	http://hdl.handle.net/11400/9070
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	http://history.icininfo.net/2011/	el
dc.source	http://history.icininfo.net/2011/	el
dc.subject	Data mining
dc.subject	Database management
dc.subject	Algorithms
dc.subject	Εξόρυξη δεδομένων
dc.subject	Διαχείρισης βάσεων δεδομένων
dc.subject	Clinical datasets
dc.subject	Κλινικά σύνολα δεδομένων
dc.subject	Αλγόριθμοι
dc.title	Applicability of data mining algoritms on clinical datasets	en
heal.type	conferenceItem
heal.classification	Computer science
heal.classification	Mathematics
heal.classification	Πληροφορική
heal.classification	Μαθηματικά
heal.classificationURI	http://skos.um.es/unescothes/C00750
heal.classificationURI	http://zbw.eu/stw/thsys/70269
heal.classificationURI	N/A-Πληροφορική
heal.classificationURI	N/A-Μαθηματικά
heal.keywordURI	http://zbw.eu/stw/descriptor/19807-1
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh85035848
heal.keywordURI	http://skos.um.es/unescothes/C00134
heal.contributorName	Γιαννακόπουλος, Γεώργιος Α. (συντ.)	el
heal.contributorName	Σακκάς, Δαμιανός Π. (συντ.)	el
heal.language	en
heal.access	free
heal.recordProvider	Τεχνολογικό Εκπαιδευτικό Ίδρυμα Αθήνας. Σχολή Διοίκησης και Οικονομίας. Τμήμα Βιβλιοθηκονομίας και Συστημάτων Πληροφόρησης	el
heal.publicationDate	2011-09
heal.bibliographicCitation	Bonney, W. (2011). Applicability of data mining algoritms on clinical datasets. International Conference on Integrated Information (IC-ININFO 2011), Kos Island, Greece, 29 September - 3 Octomber 2011. pp. 218-220. Available from: http://history.icininfo.net/2011/FileStore/procs_INFO_2011.pdf	en
heal.abstract	The essential need of database management systems to improve the quality of healthcare delivery makes the use of data mining techniques a phenomenon that cannot be ignored. Today, many healthcare providers are in the business of capturing and storing patients' personalized health information such as demographics, family history, allergies, medications, and diagnosis. This information is generally collected not only to make the healthcare practitioner well-informed about the health status of patients but also to improve the efficiency of care delivery and reduce waiting times. This paper aims to discover the applicability of data mining algorithms on clinical datasets. An experimental study was conducted to compare the performance of four different learning algorithms across four clinical datasets using 10 fold cross-validations.	en
heal.fullTextAvailability	true
heal.conferenceName	International Conference on Integrated Information (IC-ININFO 2011)	el
heal.conferenceItemType	full paper