{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T23:24:28Z","timestamp":1791501868977,"version":"4.3.4"},"reference-count":66,"publisher":"Public Library of Science (PLoS)","issue":"5","license":[{"start":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T00:00:00Z","timestamp":1682899200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100007333","name":"Alzheimer's Disease Neuroimaging Initiative","doi-asserted-by":"crossref","award":["(National Institutes of Health Grant U01 AG024904)"],"award-info":[{"award-number":["(National Institutes of Health Grant U01 AG024904)"]}],"id":[{"id":"10.13039\/100007333","id-type":"DOI","asserted-by":"crossref"}]},{"name":"DOD ADNI","award":["(Department of Defense award number W81XWH-12-2-0012)"],"award-info":[{"award-number":["(Department of Defense award number W81XWH-12-2-0012)"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>The increasing incidence of Alzheimer\u2019s 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.<\/jats:p>","DOI":"10.1371\/journal.pone.0283712","type":"journal-article","created":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T17:25:12Z","timestamp":1682961912000},"page":"e0283712","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":16,"title":["Wide and deep learning based approaches for classification of Alzheimer\u2019s disease using genome-wide association studies"],"prefix":"10.1371","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4504-1506","authenticated-orcid":true,"given":"Abbas Saad","family":"Alatrany","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7511-3873","authenticated-orcid":true,"given":"Wasiq","family":"Khan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abir","family":"Hussain","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9170-0568","authenticated-orcid":true,"given":"Dhiya","family":"Al-Jumeily","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"for the Alzheimer\u2019s Disease Neuroimaging Initiative","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2023,5,1]]},"reference":[{"key":"pone.0283712.ref001","article-title":"The global dementia observatory reference guide","author":"W. 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Kursa","year":"2010","journal-title":"Journal of statistical software"},{"key":"pone.0283712.ref038","doi-asserted-by":"crossref","first-page":"100113","DOI":"10.1109\/ACCESS.2022.3207836","article-title":"Gait Identification Using Limb Joint Movement and Deep Machine Learning","volume":"10","author":"L. K. Topham","year":"2022","journal-title":"IEEE Access"},{"key":"pone.0283712.ref039","doi-asserted-by":"crossref","first-page":"114341","DOI":"10.1016\/j.eswa.2020.114341","article-title":"Deception in the eyes of deceiver: A computer vision and machine learning based automated deception detection","volume":"169","author":"W. Khan","year":"2021","journal-title":"Expert Systems with Applications"},{"issue":"2065","key":"pone.0283712.ref040","first-page":"20150202","article-title":"Principal component analysis: a review and recent developments,\" Philosophical Transactions of the Royal Society A: Mathematical","volume":"374","author":"I. T. Jolliffe","journal-title":"Physical and Engineering Sciences"},{"issue":"4","key":"pone.0283712.ref041","doi-asserted-by":"crossref","first-page":"296","DOI":"10.2174\/157489310794072508","article-title":"A review of ensemble methods in bioinformatics","volume":"5","author":"P. Yang","year":"2010","journal-title":"Current Bioinformatics"},{"issue":"5","key":"pone.0283712.ref042","doi-asserted-by":"crossref","first-page":"5198","DOI":"10.1007\/s11227-020-03481-x","article-title":"AI-based smart prediction of clinical disease using random forest classifier and I Bayes","volume":"77","author":"V. Jackins","year":"2021","journal-title":"The Journal of Supercomputing"},{"key":"pone.0283712.ref043","doi-asserted-by":"crossref","first-page":"124647","DOI":"10.1016\/j.jhydrol.2020.124647","article-title":"Complete ensemble empirical mode decomposition hybridized with random forest and kernel ridge regression model for monthly rainfall forecasts","volume":"584","author":"M. Ali","year":"2020","journal-title":"Journal of Hydrology"},{"issue":"1","key":"pone.0283712.ref044","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"L. Breiman","year":"2001","journal-title":"Machine learning"},{"issue":"11","key":"pone.0283712.ref045","doi-asserted-by":"crossref","first-page":"e00938","DOI":"10.1016\/j.heliyon.2018.e00938","article-title":"State-of-the-art in artificial neural network applications: A survey","volume":"4","author":"O. I. Abiodun","year":"2018","journal-title":"Heliyon"},{"issue":"11","key":"pone.0283712.ref046","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11306-019-1608-0","article-title":"The application of artificial neural networks in metabolomics: a historical perspective","volume":"15","author":"K. M. 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Wilson","year":"2021","journal-title":"Computational and Structural Biotechnology Journal"},{"key":"pone.0283712.ref050","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1145\/2988450.2988454","article-title":"Wide & deep learning for recommender systems","author":"H.-T. Cheng","year":"2016","journal-title":"Proceedings of the 1st workshop on deep learning for recommender systems"},{"issue":"3","key":"pone.0283712.ref051","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1086\/519795","article-title":"PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses","volume":"81","author":"S. Purcell","year":"2007","journal-title":"The American Journal of Human Genetics"},{"key":"pone.0283712.ref052","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"F. Pedregosa","year":"2011","journal-title":"the Journal of machine Learning research"},{"key":"pone.0283712.ref053","unstructured":"L. Perreauls. \"PyPlink.\" https:\/\/lemieuxl.github.io\/pyplink\/pyplink.html. (accessed 5\/7\/2022."},{"key":"pone.0283712.ref054","volume-title":"Deep learning with Keras","author":"A. Gulli","year":"2017"},{"issue":"1","key":"pone.0283712.ref055","first-page":"1","article-title":"Mutational analysis of PRNP in Alzheimer\u2019s disease and frontotemporal dementia in China","volume":"6","author":"W. Zhang","year":"2016","journal-title":"Scientific reports"},{"issue":"5","key":"pone.0283712.ref056","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1038\/tpj.2009.69","article-title":"A TOMM40 variable-length polymorphism predicts the age of late-onset Alzheimer\u2019s disease","volume":"10","author":"A. D. Roses","year":"2010","journal-title":"The pharmacogenomics journal"},{"issue":"3","key":"pone.0283712.ref057","doi-asserted-by":"crossref","first-page":"e191350","DOI":"10.1001\/jamanetworkopen.2019.1350","article-title":"Association of rare coding mutations with Alzheimer disease and other dementias among adults of European ancestry","volume":"2","author":"D. Patel","year":"2019","journal-title":"JAMA network open"},{"key":"pone.0283712.ref058","article-title":"Minimax powerful functional analysis of covariance tests with application to longitudinal genome\u2010wide association studies","author":"W. Zhu","year":"2022","journal-title":"Scandinavian Journal of Statistics"},{"issue":"2","key":"pone.0283712.ref059","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1109\/TCBB.2018.2868667","article-title":"Utilizing Deep Learning and Genome Wide Association Studies for Epistatic-Driven Preterm Birth Classification in African-American Women","volume":"17","author":"P. Fergus","year":"2020","journal-title":"IEEE\/ACM Transactions on Computational Biology and Bioinformatics"},{"issue":"1","key":"pone.0283712.ref060","doi-asserted-by":"crossref","first-page":"e12300","DOI":"10.1002\/dad2.12300","article-title":"Machine learning methods applied to genotyping data capture interactions between single nucleotide variants in late onset Alzheimer\u2019s disease,\" Alzheimer\u2019s & Dementia: Diagnosis","volume":"14","author":"M. Arnal Segura","year":"2022","journal-title":"Assessment & Disease Monitoring"},{"issue":"2","key":"pone.0283712.ref061","article-title":"Deep learning-based identification of genetic variants: application to Alzheimer\u2019s disease classification","volume":"23","author":"T. 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