Autoencoder imputation of missing heterogeneous data for Alzheimer's disease classification

Abstract Missing Alzheimer's disease (AD) data is prevalent and poses significant challenges for AD diagnosis. Previous studies have explored various data imputation approaches on AD data, but the systematic evaluation of deep learning algorithms for imputing heterogeneous and comprehensive AD...

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Detalhes bibliográficos
Main Authors: Namitha Thalekkara Haridas, Jose M. Sanchez‐Bornot, Paula L. McClean, KongFatt Wong‐Lin, Alzheimer's Disease Neuroimaging Initiative (ADNI)
Formato: Artigo
Idioma:Inglês
Publicado em: Wiley 2024-12-01
Colecção:Healthcare Technology Letters
Assuntos:
data mining
data reduction
decision support systems
feature extraction
feature selection
learning (artificial intelligence)
Acesso em linha:https://doi.org/10.1049/htl2.12091
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