Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/3563
Full metadata record
DC FieldValueLanguage
dc.contributor.authorMazzocco, Thomas-
dc.contributor.authorHussain, Amir-
dc.date.accessioned2013-06-09T05:08:20Z-
dc.date.issued2012-02-
dc.identifier.urihttp://hdl.handle.net/1893/3563-
dc.description.abstractClinicians often experience difficulties in the diagnosis of dementia due to the intrinsic complexity of the process and lack of comprehensive diagnostic tools. Different models have been proposed to provide medical decision support in dementia diagnosis. The aim of this study is to improve on the performance of a recent application of Bayesian belief networks using an alternative approach based on logistic regression. A pool of 14 variables has been evaluated in a sample of 164 patients suspected of dementia. First, a logistic regression model for dementia prediction is developed using all variables included in the previous model; then, a second model is built using a stepwise logistic regression starting with all collected variables and selecting the pool of the relevant ones. A range of performance metrics have been used to evaluate the developed models. The new models have resulted in very good predictive power, demonstrating general performance improvement compared to a state-of-the-art prediction model. Interestingly, the approach based on statistical variables selection outperformed the model which used variables selected by domain experts in the previous study. Further collaborative studies are now required to determine the optimal approach and to overcome existing limitations imposed by the size of the considered sample.en_UK
dc.language.isoen-
dc.publisherElsevier-
dc.relationMazzocco T & Hussain A (2012) Novel logistic regression models to aid the diagnosis of dementia, Expert Systems with Applications, 39 (3), pp. 3356-3361.-
dc.rightsThe publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author; you can only request a copy if you wish to use this work for your own research or private study.-
dc.subjectDementiaen_UK
dc.subjectDiagnosisen_UK
dc.subjectVariables selectionen_UK
dc.subjectLogistic regressionen_UK
dc.subjectPrediction modelen_UK
dc.subjectDecision support systemen_UK
dc.subject.lcshDementia Diagnoses-
dc.subject.lcshRegression analysis Mathematical models-
dc.titleNovel logistic regression models to aid the diagnosis of dementiaen_UK
dc.typeJournal Articleen_UK
dc.rights.embargodate2999-12-31T00:00:00Z-
dc.rights.embargoreasonThe publisher does not allow this work to be made publicly available in this Repository therefore there is an embargo on the full text of the work.-
dc.identifier.doihttp://dx.doi.org/10.1016/j.eswa.2011.09.023-
dc.citation.jtitleExpert Systems with Applications-
dc.citation.issn0957-4174-
dc.citation.volume39-
dc.citation.issue3-
dc.citation.spage3356-
dc.citation.epage3361-
dc.citation.publicationstatusPublished-
dc.citation.peerreviewedRefereed-
dc.type.statusPublisher version (final published refereed version)-
dc.author.emailahu@cs.stir.ac.uk-
dc.contributor.affiliationUniversity of Stirling-
dc.contributor.affiliationComputing Science - CSM Dept-
dc.rights.embargoterms2999-12-31-
dc.rights.embargoliftdate2999-12-31-
dc.identifier.isi000297823300118-
Appears in Collections:Computing Science and Mathematics Journal Articles

Files in This Item:
File Description SizeFormat 
Novel logistic regression models.pdf375.93 kBAdobe PDFUnder Permanent Embargo    Request a copy


This item is protected by original copyright



Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.

If you believe that any material held in STORRE infringes copyright, please contact library@stir.ac.uk providing details and we will remove the Work from public display in STORRE and investigate your claim.