Wrappers feature selection in alzheimer’s biomarkers using kNN and SMOTE oversampling
Autor(a) principal: | |
---|---|
Data de Publicação: | 2017 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFRGS |
Texto Completo: | http://hdl.handle.net/10183/163334 |
Resumo: | Biomarkers are characteristics that are objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes or pharmacological responses to a therapeutic intervention. The combination of different biomarker modalities often allows an accurate diagnosis classification. In Alzheimer’s disease (AD), biomarkers are indispensable to identify cognitively normal individuals destined to develop dementia symptoms.However, using the combination of canonicalAD biomarkers, studies have repeatedly shown poor classification rates to differentiate between AD, mild cognitive impairment and control individuals. Furthermore, the design of classifiers to access multiple biomarker combinations includes issues such as imbalance classes and missing data. Due to the number of biomarkers combinations wrappers are used to avoid multiple comparisons. Here, we compare the ability of three wrappers feature selection methods to obtain biomarker combinations which maximize classification rates. Also, as the criterion to the wrappers feature selection we use the k-nearest neighbor classifier with balance aids, random undersampling and SMOTE oversampling. Overall, our analyses showed how biomarkers combinations affect the classifier precision and how imbalance strategy improve it.We show that non-defining and non-cognitive biomarkers have less precision than cognitive measures when classifying AD. Our approach surpasses in average the support vector machine and the weighted k-nearest neighbor classifiers and reaches 94.34 ± 3.91% of precision reproducing class definitions. |
id |
UFRGS-2_588ec2a26c3d5f374a307fe6e0f24951 |
---|---|
oai_identifier_str |
oai:www.lume.ufrgs.br:10183/163334 |
network_acronym_str |
UFRGS-2 |
network_name_str |
Repositório Institucional da UFRGS |
repository_id_str |
|
spelling |
Rodrigues, Yuri EliasManica, EvandroZimmer, Eduardo RigonPascoal, Tharick AliMathotaarachchi, Sulantha SanjeewaRosa Neto, Pedro2017-06-22T02:42:59Z20171677-1966http://hdl.handle.net/10183/163334001022889Biomarkers are characteristics that are objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes or pharmacological responses to a therapeutic intervention. The combination of different biomarker modalities often allows an accurate diagnosis classification. In Alzheimer’s disease (AD), biomarkers are indispensable to identify cognitively normal individuals destined to develop dementia symptoms.However, using the combination of canonicalAD biomarkers, studies have repeatedly shown poor classification rates to differentiate between AD, mild cognitive impairment and control individuals. Furthermore, the design of classifiers to access multiple biomarker combinations includes issues such as imbalance classes and missing data. Due to the number of biomarkers combinations wrappers are used to avoid multiple comparisons. Here, we compare the ability of three wrappers feature selection methods to obtain biomarker combinations which maximize classification rates. Also, as the criterion to the wrappers feature selection we use the k-nearest neighbor classifier with balance aids, random undersampling and SMOTE oversampling. Overall, our analyses showed how biomarkers combinations affect the classifier precision and how imbalance strategy improve it.We show that non-defining and non-cognitive biomarkers have less precision than cognitive measures when classifying AD. Our approach surpasses in average the support vector machine and the weighted k-nearest neighbor classifiers and reaches 94.34 ± 3.91% of precision reproducing class definitions.application/pdfengTEMA : tendências em matemática aplicada e computacional. São Carlos. Vol. 18, no. 1 (2017), p. 15-34Modelagem matemáticak-nearest neighborSMOTEFeature selectionAlzheimer’s biomarkersAlzheimer’s disease classificationWrappers feature selection in alzheimer’s biomarkers using kNN and SMOTE oversamplinginfo:eu-repo/semantics/articleinfo:eu-repo/semantics/otherinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFRGSinstname:Universidade Federal do Rio Grande do Sul (UFRGS)instacron:UFRGSORIGINAL001022889.pdf001022889.pdfTexto completo (inglês)application/pdf1915068http://www.lume.ufrgs.br/bitstream/10183/163334/1/001022889.pdf9b4805bd98bf3f171bc11b9d902f536dMD51TEXT001022889.pdf.txt001022889.pdf.txtExtracted Texttext/plain58276http://www.lume.ufrgs.br/bitstream/10183/163334/2/001022889.pdf.txtdea6bf77a2b9ac802e7c101abc6f0c1eMD52THUMBNAIL001022889.pdf.jpg001022889.pdf.jpgGenerated Thumbnailimage/jpeg1585http://www.lume.ufrgs.br/bitstream/10183/163334/3/001022889.pdf.jpgc8751d8282c17eb89141463c7a47a113MD5310183/1633342021-09-18 04:53:34.121122oai:www.lume.ufrgs.br:10183/163334Repositório InstitucionalPUBhttps://lume.ufrgs.br/oai/requestlume@ufrgs.bropendoar:2021-09-18T07:53:34Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false |
dc.title.pt_BR.fl_str_mv |
Wrappers feature selection in alzheimer’s biomarkers using kNN and SMOTE oversampling |
title |
Wrappers feature selection in alzheimer’s biomarkers using kNN and SMOTE oversampling |
spellingShingle |
Wrappers feature selection in alzheimer’s biomarkers using kNN and SMOTE oversampling Rodrigues, Yuri Elias Modelagem matemática k-nearest neighbor SMOTE Feature selection Alzheimer’s biomarkers Alzheimer’s disease classification |
title_short |
Wrappers feature selection in alzheimer’s biomarkers using kNN and SMOTE oversampling |
title_full |
Wrappers feature selection in alzheimer’s biomarkers using kNN and SMOTE oversampling |
title_fullStr |
Wrappers feature selection in alzheimer’s biomarkers using kNN and SMOTE oversampling |
title_full_unstemmed |
Wrappers feature selection in alzheimer’s biomarkers using kNN and SMOTE oversampling |
title_sort |
Wrappers feature selection in alzheimer’s biomarkers using kNN and SMOTE oversampling |
author |
Rodrigues, Yuri Elias |
author_facet |
Rodrigues, Yuri Elias Manica, Evandro Zimmer, Eduardo Rigon Pascoal, Tharick Ali Mathotaarachchi, Sulantha Sanjeewa Rosa Neto, Pedro |
author_role |
author |
author2 |
Manica, Evandro Zimmer, Eduardo Rigon Pascoal, Tharick Ali Mathotaarachchi, Sulantha Sanjeewa Rosa Neto, Pedro |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Rodrigues, Yuri Elias Manica, Evandro Zimmer, Eduardo Rigon Pascoal, Tharick Ali Mathotaarachchi, Sulantha Sanjeewa Rosa Neto, Pedro |
dc.subject.por.fl_str_mv |
Modelagem matemática |
topic |
Modelagem matemática k-nearest neighbor SMOTE Feature selection Alzheimer’s biomarkers Alzheimer’s disease classification |
dc.subject.eng.fl_str_mv |
k-nearest neighbor SMOTE Feature selection Alzheimer’s biomarkers Alzheimer’s disease classification |
description |
Biomarkers are characteristics that are objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes or pharmacological responses to a therapeutic intervention. The combination of different biomarker modalities often allows an accurate diagnosis classification. In Alzheimer’s disease (AD), biomarkers are indispensable to identify cognitively normal individuals destined to develop dementia symptoms.However, using the combination of canonicalAD biomarkers, studies have repeatedly shown poor classification rates to differentiate between AD, mild cognitive impairment and control individuals. Furthermore, the design of classifiers to access multiple biomarker combinations includes issues such as imbalance classes and missing data. Due to the number of biomarkers combinations wrappers are used to avoid multiple comparisons. Here, we compare the ability of three wrappers feature selection methods to obtain biomarker combinations which maximize classification rates. Also, as the criterion to the wrappers feature selection we use the k-nearest neighbor classifier with balance aids, random undersampling and SMOTE oversampling. Overall, our analyses showed how biomarkers combinations affect the classifier precision and how imbalance strategy improve it.We show that non-defining and non-cognitive biomarkers have less precision than cognitive measures when classifying AD. Our approach surpasses in average the support vector machine and the weighted k-nearest neighbor classifiers and reaches 94.34 ± 3.91% of precision reproducing class definitions. |
publishDate |
2017 |
dc.date.accessioned.fl_str_mv |
2017-06-22T02:42:59Z |
dc.date.issued.fl_str_mv |
2017 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/other |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10183/163334 |
dc.identifier.issn.pt_BR.fl_str_mv |
1677-1966 |
dc.identifier.nrb.pt_BR.fl_str_mv |
001022889 |
identifier_str_mv |
1677-1966 001022889 |
url |
http://hdl.handle.net/10183/163334 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartof.pt_BR.fl_str_mv |
TEMA : tendências em matemática aplicada e computacional. São Carlos. Vol. 18, no. 1 (2017), p. 15-34 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da UFRGS instname:Universidade Federal do Rio Grande do Sul (UFRGS) instacron:UFRGS |
instname_str |
Universidade Federal do Rio Grande do Sul (UFRGS) |
instacron_str |
UFRGS |
institution |
UFRGS |
reponame_str |
Repositório Institucional da UFRGS |
collection |
Repositório Institucional da UFRGS |
bitstream.url.fl_str_mv |
http://www.lume.ufrgs.br/bitstream/10183/163334/1/001022889.pdf http://www.lume.ufrgs.br/bitstream/10183/163334/2/001022889.pdf.txt http://www.lume.ufrgs.br/bitstream/10183/163334/3/001022889.pdf.jpg |
bitstream.checksum.fl_str_mv |
9b4805bd98bf3f171bc11b9d902f536d dea6bf77a2b9ac802e7c101abc6f0c1e c8751d8282c17eb89141463c7a47a113 |
bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 |
repository.name.fl_str_mv |
Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS) |
repository.mail.fl_str_mv |
lume@ufrgs.br |
_version_ |
1817725000845623296 |