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

Author

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  • Abbas Saad Alatrany
  • Wasiq Khan
  • Abir Hussain
  • Dhiya Al-Jumeily
  • for the Alzheimer’s Disease Neuroimaging Initiative

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.

Suggested Citation

  • Abbas Saad Alatrany & Wasiq Khan & Abir Hussain & Dhiya Al-Jumeily & for the Alzheimer’s Disease Neuroimaging Initiative, 2023. "Wide and deep learning based approaches for classification of Alzheimer’s disease using genome-wide association studies," PLOS ONE, Public Library of Science, vol. 18(5), pages 1-21, May.
    • Handle: RePEc:plo:pone00:0283712
    DOI: 10.1371/journal.pone.0283712
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    References listed on IDEAS

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    1. Kursa, Miron B. & Rudnicki, Witold R., 2010. "Feature Selection with the Boruta Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i11).
    2. Qian Zhang & Julia Sidorenko & Baptiste Couvy-Duchesne & Riccardo E. Marioni & Margaret J. Wright & Alison M. Goate & Edoardo Marcora & Kuan-lin Huang & Tenielle Porter & Simon M. Laws & Perminder S. , 2020. "Risk prediction of late-onset Alzheimer’s disease implies an oligogenic architecture," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    3. Teri A. Manolio & Francis S. Collins & Nancy J. Cox & David B. Goldstein & Lucia A. Hindorff & David J. Hunter & Mark I. McCarthy & Erin M. Ramos & Lon R. Cardon & Aravinda Chakravarti & Judy H. Cho &, 2009. "Finding the missing heritability of complex diseases," Nature, Nature, vol. 461(7265), pages 747-753, October.
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