Wrappers Feature Selection in Alzheimer's Biomarkers Using kNN and SMOTE Oversampling
DOI:
https://doi.org/10.5540/tema.2017.018.01.0015Keywords:
k-vizinhos mais próximos, SMOTE, seleção de características, biomarcadores de Alzheimer, problema de classificaçãoAbstract
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.
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