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Alatrany, AS, Khan, W, Hussain, A and Al-Jumeily, D (2023) Wide and deep learning based approaches for classification of Alzheimer's disease using genome-wide association studies. PLoS One, 18 (5). ISSN 1932-6203
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Wide and deep learning based approaches for classification of Alzheimers disease using genome-wide association studies.pdf - Published Version Available under License Creative Commons Attribution. Download (1MB) | Preview |
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 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.
| Item Type: | Article |
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| Uncontrolled Keywords: | Alzheimer’s Disease Neuroimaging Initiative; Humans; Alzheimer Disease; Magnetic Resonance Imaging; Genome-Wide Association Study; Deep Learning; Neural Networks, Computer; Humans; Deep Learning; Magnetic Resonance Imaging; Genome-Wide Association Study; Alzheimer Disease; Neural Networks, Computer; General Science & Technology |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry |
| Divisions: | Computer Science & Mathematics |
| Publisher: | Public Library of Science |
| SWORD Depositor: | A Symplectic |
| Date Deposited: | 29 Jun 2023 13:31 |
| Last Modified: | 29 Jun 2023 13:45 |
| DOI or ID number: | 10.1371/journal.pone.0283712 |
| Editors: | Hammad, M |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/20142 |
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