Wide and deep learning based approaches for classification of Alzheimer's disease using genome-wide association studies.

ABSTRACT: The increasing incidence of Alzheimer's disease (AD) has been leading towards a significant growth in socioeconomic challenges. A reliable prediction of AD might be useful to mitigate or at-least slow down its progression for which, identification of the factors affecting the AD and its accurate diagnoses, are vital. In this study, we use Genome-Wide Association Studies (GWAS) dataset which comprises significant genetic markers of complex diseases. The original dataset contains large number of attributes (620901) for which we propose a hybrid feature selection approach based on association test, principal component analysis, and the Boruta algorithm, to identify the most promising predictors of AD. The selected features are then forwarded to a wide and deep neural network models to classif

SUBMITTER: Alatrany AS 

PROVIDER: S-EPMC10150974 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature