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Wrappers feature selection in alzheimer’s biomarkers using kNN and SMOTE oversampling

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Wrappers feature selection in alzheimer’s biomarkers using kNN and SMOTE oversampling

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Título Wrappers feature selection in alzheimer’s biomarkers using kNN and SMOTE oversampling
Autor Rodrigues, Yuri Elias
Manica, Evandro
Zimmer, Eduardo Rigon
Pascoal, Tharick Ali
Mathotaarachchi, Sulantha Sanjeewa
Rosa Neto, Pedro
Abstract 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.
Contido em TEMA : tendências em matemática aplicada e computacional. São Carlos. Vol. 18, no. 1 (2017), p. 15-34
Assunto Modelagem matemática
[en] Alzheimer’s biomarkers
[en] Alzheimer’s disease classification
[en] Feature selection
[en] k-nearest neighbor
[en] SMOTE
Origem Nacional
Tipo Artigo de periódico
URI http://hdl.handle.net/10183/163334
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