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dc.contributor.authorRodrigues, Yuri Eliaspt_BR
dc.contributor.authorManica, Evandropt_BR
dc.contributor.authorZimmer, Eduardo Rigonpt_BR
dc.contributor.authorPascoal, Tharick Alipt_BR
dc.contributor.authorMathotaarachchi, Sulantha Sanjeewapt_BR
dc.contributor.authorRosa Neto, Pedropt_BR
dc.date.accessioned2017-06-22T02:42:59Zpt_BR
dc.date.issued2017pt_BR
dc.identifier.issn1677-1966pt_BR
dc.identifier.urihttp://hdl.handle.net/10183/163334pt_BR
dc.description.abstractBiomarkers 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.en
dc.format.mimetypeapplication/pdfpt_BR
dc.language.isoengpt_BR
dc.relation.ispartofTEMA : tendências em matemática aplicada e computacional. São Carlos. Vol. 18, no. 1 (2017), p. 15-34pt_BR
dc.rightsOpen Accessen
dc.subjectModelagem matemáticapt_BR
dc.subjectk-nearest neighboren
dc.subjectSMOTEen
dc.subjectFeature selectionen
dc.subjectAlzheimer’s biomarkersen
dc.subjectAlzheimer’s disease classificationen
dc.titleWrappers feature selection in alzheimer’s biomarkers using kNN and SMOTE oversamplingpt_BR
dc.typeArtigo de periódicopt_BR
dc.identifier.nrb001022889pt_BR
dc.type.originNacionalpt_BR


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