Zobrazit minimální záznam

dc.contributor.authorOwn, Hala S.
dc.contributor.authorAbraham, Ajith
dc.date.accessioned2016-10-14T08:26:58Z
dc.date.available2016-10-14T08:26:58Z
dc.date.issued2012
dc.identifier.citationApplied Soft Computing. 2012, vol. 12, issue 3, p. 999-1005.cs
dc.identifier.issn1568-4946
dc.identifier.urihttp://hdl.handle.net/10084/112162
dc.description.abstractPrediction of diseases would help physicians to make informal decision regarding the type of treatment. Jaundice is the most common condition that requires medical attention in newborn babies. Although most newborns develop some degree of jaundice, a high level bilirubin puts a newborn at risk of bilirubin encephalopathy and kernicterus, which are rare but still occur in Egypt. This paper presents a new weighted rough set framework for early intervention and prevention of neurological dysfunction and kernicterus that are catastrophic sequels of neonatal jaundice. The obtained results illustrate that the weighted rough set can provide significantly more accurate and reliable predictive accuracy than well known algorithms such as weighted SVM and decision tree considering the fact that physicians do not have any estimation about probability of jaundice appearance.cs
dc.language.isoencs
dc.publisherElseviercs
dc.relation.ispartofseriesApplied Soft Computingcs
dc.relation.urihttp://dx.doi.org/10.1016/j.asoc.2011.11.025cs
dc.rightsCopyright © 2011 Elsevier B.V. All rights reserved.cs
dc.subjectweighted rough setcs
dc.subjectneonatal jaundicecs
dc.subjectclass imbalance learningcs
dc.subjectrule importancecs
dc.titleA new weighted rough set framework based classification for Egyptian NeoNatal Jaundicecs
dc.typearticlecs
dc.identifier.doi10.1016/j.asoc.2011.11.025
dc.type.statusPeer-reviewedcs
dc.description.sourceWeb of Sciencecs
dc.description.volume12cs
dc.description.issue3cs
dc.description.lastpage1005cs
dc.description.firstpage999cs
dc.identifier.wos000299324200003


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Zobrazit minimální záznam