Logo do repositório
 
Publicação

Prediction of dementia patients: A comparative approach using parametric vs. non parametric classifiers

dc.contributor.authorMaroco, João
dc.contributor.authorSilva, Dina Lúcia Gomes da
dc.contributor.authorGuerreiro, Manuela
dc.contributor.authorMendonça, Alexandre de
dc.contributor.authorSantana, Isabel
dc.date.accessioned2012-09-12T18:29:38Z
dc.date.available2012-09-12T18:29:38Z
dc.date.issued2012
dc.description.abstractIn this paper, we report a comparison study of 7 non parametric classifiers (Multilayer perceptron Neural Networks, Radial Basis Function Neural Networks, SupportVectorMachines, CART, CHAID and QUEST Classification trees and Random Forests) as compared to Linear Discriminant Analysis, Quadratic Discriminant Analysis and Logistic Regression tested in a real data application of mild cognitive impaired elderly patients conversion to dementia. When classification results are compared both on overall accuracy, specificity and sensitivity, Linear Discriminant Analysis and Random Forests rank first among all the classifiers.por
dc.identifier.citationIn Actas do XVII Congresso Anual da Sociedade Portuguesa de Estatística (pp. 241-251). Lisboa: Sociedade Portuguesa de Estatísticapor
dc.identifier.urihttp://hdl.handle.net/10400.12/1691
dc.language.isoengpor
dc.peerreviewednopor
dc.publisherSociedade Portuguesa de Estatísticapor
dc.titlePrediction of dementia patients: A comparative approach using parametric vs. non parametric classifierspor
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlaceLisboapor
oaire.citation.endPage251por
oaire.citation.startPage241por
oaire.citation.titleXVII Congresso Anual da Sociedade Portuguesa de Estatísticapor
rcaap.rightsrestrictedAccesspor
rcaap.typeconferenceObjectpor

Ficheiros

Principais
A mostrar 1 - 1 de 1
A carregar...
Miniatura
Nome:
SPE 2012 in press (actas).pdf
Tamanho:
2.07 MB
Formato:
Adobe Portable Document Format
Licença
A mostrar 1 - 1 de 1
Miniatura indisponível
Nome:
license.txt
Tamanho:
1.71 KB
Formato:
Item-specific license agreed upon to submission
Descrição: