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dc.contributor.authorCaon, Federicoen_US
dc.contributor.authorMeneghel, Gaiaen_US
dc.contributor.authorZaghi, Piercarloen_US
dc.contributor.authorDe Leo, Diegoen_US
dc.date.accessioned2017-04-24T11:31:50Z
dc.date.available2017-04-24T11:31:50Z
dc.date.issued2002en_US
dc.date.modified2010-09-24T06:48:53Z
dc.identifier.issn13811118en_US
dc.identifier.doi10.1080/13811110214139en_AU
dc.identifier.urihttp://hdl.handle.net/10072/6572
dc.description.abstractSuicide is a leading cause of death internationally. Prediction of the phenomenon has proved to be extremely difficult and results in this area have been modest. The objective of this pilot study was to assess the applicability of the Neural Network statistical procedure to suicidal data. The predictive ability of three statistical techniques (Logistic Regression, Discriminatory Analysis and Neural Networks) were compared in the ability to distinguish between suicides and attempts on the basis of a variety of variables. Although the Neural Network model expresses its full potential with large numbers, it has proved to be effective even in our limited sample. This suggests that neural networks may exhibit superior predictive capacity when the data set is enlarged. This technique may prove to be a useful additional tool in the field of suicide prediction and prevention.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherBrunner-Routledge, Taylor & Francis Groupen_US
dc.publisher.placeUnited Kingdomen_US
dc.relation.ispartofpagefrom285en_US
dc.relation.ispartofpageto289en_US
dc.relation.ispartofjournalArchives of Suicide Researchen_US
dc.relation.ispartofvolume6en_US
dc.subject.fieldofresearchcode321299en_US
dc.titleApplicability of neural networks to suicidological research: A pilot studyen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.date.issued2002
gro.hasfulltextNo Full Text


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