Wrappers Feature Selection in Alzheimer's Biomarkers Using kNN and SMOTE Oversampling

Autores

  • Yuri Elias Rodrigues Federal University of Rio Grande do Sul (UFRGS)
  • Evandro Manica Federal University of Rio Grande do Sul
  • Eduardo Rigon Zimmer Institue Inscer, Catholic University of Rio Grande do Sul; Brain Federal University of Rio Grande do Sul
  • Tharick Ali Pascoal Translational Neuroimaging Laboratory, McGill University, Montréal
  • Sulantha Sanjeewa Mathotaarachchi Translational Neuroimaging Laboratory, McGill University, Montréal
  • Pedro Rosa-Neto Translational Neuroimaging Laboratory, McGill University, Montréal

DOI:

https://doi.org/10.5540/tema.2017.018.01.0015

Palavras-chave:

k-vizinhos mais próximos, SMOTE, seleção de características, biomarcadores de Alzheimer, problema de classificação

Resumo

Biomarkers are a characteristic that is objectively measured and eval-
uated as an indicator of normal biological processes, pathogenic processes or phar-
macological responses to a therapeutic intervention. The combination of dierent
biomarker modalities often allows an accurate diagnosis classication. In Alzheimer's
disease (AD), biomarkers are indispensable to identify cognitively normal individ-
uals destined to develop dementia symptoms. However, using the combination of
canonical AD biomarkers, studies have repeatedly shown poor classication rates
to dierentiate between AD, mild cognitive impairment and control individuals.
Furthermore, the design of classiers to access multiple biomarker combinations
includes issues such as imbalance classes and missing data. Since the number
biomarker combinations is large then wrappers are used to avoid multiple com-
parisons. Here, we compare the ability of three wrappers feature selection methods
to obtain biomarker combinations which maximize classication rates. Also, as
criterion to the wrappers feature selection we use the k-nearest neighbor classi-
er with balance aids, random undersampling and SMOTE. Overall, our analyses
showed how biomarkers combinations aects the classier accuracy and how imbal-
ance strategy improve it. We show that non-dening and non-cognitive biomarkers
have less accuracy than cognitive measures when classifying AD. Our approach sur-
pass in average the support vector machine and the weighted k-nearest neighbors
classiers and reaches 94.34 ± 3.91% of accuracy reproducing class denitions.

Biografia do Autor

Yuri Elias Rodrigues, Federal University of Rio Grande do Sul (UFRGS)

Student at Post-Graduated Program of Applied Mathematics (PPGMAP) in Mathematics Institute at Federal University of Rio Grande do Sul (UFRGS)

Evandro Manica, Federal University of Rio Grande do Sul

Associated Professor at Mathematical Institute

Eduardo Rigon Zimmer, Institue Inscer, Catholic University of Rio Grande do Sul; Brain Federal University of Rio Grande do Sul

Postdoctoral Researcher in Biochemestry at Brain Institute (InsCer)

Tharick Ali Pascoal, Translational Neuroimaging Laboratory, McGill University, Montréal

M.D, Neurologist at Translational Neuroimaging Laboratory

Pedro Rosa-Neto, Translational Neuroimaging Laboratory, McGill University, Montréal

Director of the TNL, Assistant Professor at McGill University

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Publicado

2017-05-22

Como Citar

Rodrigues, Y. E., Manica, E., Zimmer, E. R., Pascoal, T. A., Mathotaarachchi, S. S., & Rosa-Neto, P. (2017). Wrappers Feature Selection in Alzheimer’s Biomarkers Using kNN and SMOTE Oversampling. Trends in Computational and Applied Mathematics, 18(1), 15. https://doi.org/10.5540/tema.2017.018.01.0015

Edição

v. 18 n. 1 (2017)

Seção

Artigo Original

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