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

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 accurat...

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Bibliographic Details
Published in:PloS one 2023-05, Vol.18 (5), p.e0283712
Main Authors: Alatrany, Abbas Saad, Khan, Wasiq, Hussain, Abir, Al-Jumeily, Dhiya
Format: Article
Language:English
Subjects:
Accuracy
Advertising executives
Algorithms
Alzheimer Disease - genetics
Alzheimer's disease
Alzheimers disease
Analysis
Artificial neural networks
Biology and Life Sciences
Chronic diseases
Classification
Computational linguistics
Computer and Information Sciences
Datasets
Deep Learning
Dementia
Diagnosis
Feature selection
Genetic aspects
Genetic markers
Genome-wide association studies
Genome-Wide Association Study - methods
Genomes
Genomics
Humans
Illnesses
Language processing
Machine learning
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Medical research
Medicine and Health Sciences
Medicine, Experimental
Natural language interfaces
Neural networks
Neural Networks, Computer
Neurodegenerative diseases
Physical Sciences
Principal components analysis
Quality control
Research and Analysis Methods
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Summary: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 classify the AD cases and healthy controls. The experimental outcomes indicate that our approach outperformed the existing methods when evaluated on standard dataset, producing an accuracy and f1-score of 99%. The outcomes from this study are impactful particularly, the identified features comprising AD-associated genes and a reliable classification model that might be useful for other chronic diseases.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0283712