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Machine learning approaches and applications in genome wide association study for Alzheimer’s Disease: A systematic review

Alatrany, AS, Hussain, A, Mustafina, J and Al-Jumeily, D (2022) Machine learning approaches and applications in genome wide association study for Alzheimer’s Disease: A systematic review. IEEE Access, 10. pp. 62831-62847.

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Machine learning algorithms have been used for detection (and possibly) prediction of Alzheimer’s disease using genotype information, with the potential to enhance the outcome prediction. However, detailed research about the analysis and the detection of Alzheimer’s disease using genetic data is still in its primitive stage. The aim of this paper was to evaluate the scientific literature on the use of various machine learning approaches for the prediction of Alzheimer’s disease based solely on genetic data. To identify gaps in the literature, critically appraise the reporting and methods of the algorithms, and provide the foundation for a wider research programme focused on developing novel machine learning based predictive algorithms in Alzheimer’s disease. A systematic review of quantitative studies was conducted using three search engines (PubMed, Web of Science and Scopus), and included studies between 1st of January 2010 and 31st December 2021. Keywords used were ‘Alzheimer’s disease(s)’, ‘GWAS, ‘Artificial intelligence’ and their synonyms. After applying the inclusion/exclusion criteria, 24 studies were included. Machine learning methods in the reviewed papers performed in a wide range of ways (0.59 to 0.98 AUC). The main findings showed that high risk of bias in the analysis can be linked to feature selection, hyperparameter search and validation methods.

Item Type: Article
Additional Information: © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords: 08 Information and Computing Sciences; 09 Engineering; 10 Technology
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > R Medicine (General)
Divisions: Computer Science & Mathematics
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
SWORD Depositor: A Symplectic
Date Deposited: 29 Jun 2022 10:15
Last Modified: 29 Jun 2022 10:15
DOI or ID number: 10.1109/access.2022.3182543
URI: https://researchonline.ljmu.ac.uk/id/eprint/17173
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