Machine Learning Approaches and Applications in Genome Wide Association Study for Alzheimer’s Disease: A Systematic Review
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 usi...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9794643/ |
| _version_ | 1811240810374496256 |
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| author | Abbas Saad Alatrany Abir Jaafar Hussain Jamila Mustafina Dhiya Al-Jumeily |
| author_facet | Abbas Saad Alatrany Abir Jaafar Hussain Jamila Mustafina Dhiya Al-Jumeily |
| author_sort | Abbas Saad Alatrany |
| collection | DOAJ |
| description | 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 <inline-formula> <tex-math notation="LaTeX">$1^{\mathrm {st}}$ </tex-math></inline-formula> of January 2010 and <inline-formula> <tex-math notation="LaTeX">$31^{\mathrm {st}}$ </tex-math></inline-formula> 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. |
| first_indexed | 2024-04-12T13:28:00Z |
| format | Article |
| id | doaj.art-049a76fc49ff40faaa3cb51c130c2a35 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T13:28:00Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-049a76fc49ff40faaa3cb51c130c2a352022-12-22T03:31:16ZengIEEEIEEE Access2169-35362022-01-0110628316284710.1109/ACCESS.2022.31825439794643Machine Learning Approaches and Applications in Genome Wide Association Study for Alzheimer’s Disease: A Systematic ReviewAbbas Saad Alatrany0https://orcid.org/0000-0002-4504-1506Abir Jaafar Hussain1https://orcid.org/0000-0001-8413-0045Jamila Mustafina2Dhiya Al-Jumeily3https://orcid.org/0000-0002-9170-0568School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool, U.KSchool of Computer Science and Mathematics, Liverpool John Moores University, Liverpool, U.KNaberezhnochelninsky Institute, Kazan Federal University, Kazan, RussiaSchool of Computer Science and Mathematics, Liverpool John Moores University, Liverpool, U.KMachine 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 <inline-formula> <tex-math notation="LaTeX">$1^{\mathrm {st}}$ </tex-math></inline-formula> of January 2010 and <inline-formula> <tex-math notation="LaTeX">$31^{\mathrm {st}}$ </tex-math></inline-formula> 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.https://ieeexplore.ieee.org/document/9794643/Machine learningsystematic reviewAlzheimer’s diseaseGWASSNPgenome wide |
| spellingShingle | Abbas Saad Alatrany Abir Jaafar Hussain Jamila Mustafina Dhiya Al-Jumeily Machine Learning Approaches and Applications in Genome Wide Association Study for Alzheimer’s Disease: A Systematic Review IEEE Access Machine learning systematic review Alzheimer’s disease GWAS SNP genome wide |
| title | Machine Learning Approaches and Applications in Genome Wide Association Study for Alzheimer’s Disease: A Systematic Review |
| title_full | Machine Learning Approaches and Applications in Genome Wide Association Study for Alzheimer’s Disease: A Systematic Review |
| title_fullStr | Machine Learning Approaches and Applications in Genome Wide Association Study for Alzheimer’s Disease: A Systematic Review |
| title_full_unstemmed | Machine Learning Approaches and Applications in Genome Wide Association Study for Alzheimer’s Disease: A Systematic Review |
| title_short | Machine Learning Approaches and Applications in Genome Wide Association Study for Alzheimer’s Disease: A Systematic Review |
| title_sort | machine learning approaches and applications in genome wide association study for alzheimer x2019 s disease a systematic review |
| topic | Machine learning systematic review Alzheimer’s disease GWAS SNP genome wide |
| url | https://ieeexplore.ieee.org/document/9794643/ |
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