Explainable Machine Learning Model for Alzheimer Detection Using Genetic Data: A Genome-Wide Association Study Approach Article Swipe
Tarek Khater
,
Sam Ansari
,
Abbas Saad Alatrany
,
Haya Alaskar
,
Soliman A. Mahmoud
,
Ayad Turky
,
Hissam Tawfik
,
Eqab Almajali
,
Abir Hussain
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3410135
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3410135
Recent research has revealed that using machine learning systems for the analysis of genetic data could reliably detect Alzheimer’s disease. The interpretability of these models, however, has been a challenge, as they frequently provided little insight into the features that contribute to their predictions. Explainable machine learning has been presented as a solution to this problem since it enables the identification of significant attributes and gives a clearer method of making predictions. In this study, Genome-Wide Association Studies were used to recognize genetic variants associated with Alzheimer’s disease, utilizing the Alzheimer’s Disease Neuroimaging Initiative dataset and quality control methods to ensure the validity and reliability of the findings. The results indicate strong connections between certain genetic variations and Alzheimer’s disease, highlighting the potential of Genome-Wide Association Studies as a valuable tool for identifying and predicting this disease. After studying and analyzing the genetic data, machine learning algorithms are utilized to train a model to detect Alzheimer. The Support Vector Machine achieved 89% accuracy as the best-performing model. Explainable machine learning has the potential to increase the accuracy and interpretability of Alzheimer’s disease detection models, giving significant insights for both academics and physicians. The explanation of the support vector machine model reveals that rs4821510 is the most important SNP in detecting AD. On top of that, the SHAP method shows that rs429358 is an indication for Alzheimer’s disease and rs4821510 presents in the healthy ones. These findings suggest that explainable machine learning can play an important role in accurately detecting Alzheimer’s disease and identifying critical genetic markers associated with the disease.
Related Topics
Concepts
Computer science
Artificial intelligence
Machine learning
Genome-wide association study
Association (psychology)
Data modeling
Genetic data
Psychology
Medicine
Genotype
Single-nucleotide polymorphism
Gene
Genetics
Biology
Population
Environmental health
Database
Psychotherapist
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2024.3410135
- OA Status
- gold
- Cited By
- 8
- References
- 46
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4399452793
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4399452793Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2024.3410135Digital Object Identifier
- Title
-
Explainable Machine Learning Model for Alzheimer Detection Using Genetic Data: A Genome-Wide Association Study ApproachWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2024Year of publication
- Publication date
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2024-01-01Full publication date if available
- Authors
-
Tarek Khater, Sam Ansari, Abbas Saad Alatrany, Haya Alaskar, Soliman A. Mahmoud, Ayad Turky, Hissam Tawfik, Eqab Almajali, Abir HussainList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2024.3410135Publisher landing page
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://doi.org/10.1109/access.2024.3410135Direct OA link when available
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Computer science, Artificial intelligence, Machine learning, Genome-wide association study, Association (psychology), Data modeling, Genetic data, Psychology, Medicine, Genotype, Single-nucleotide polymorphism, Gene, Genetics, Biology, Population, Environmental health, Database, PsychotherapistTop concepts (fields/topics) attached by OpenAlex
- Cited by
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8Total citation count in OpenAlex
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2025: 4, 2024: 4Per-year citation counts (last 5 years)
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46Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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