Comparison of Machine Learning Algorithms for Automatic Prediction of Alzheimer Disease

dc.contributor.author Aslan, Emrah
dc.contributor.author Ozupak, Yildirim
dc.date.accessioned 2025-03-15T19:50:28Z
dc.date.accessioned 2025-09-17T14:28:33Z
dc.date.available 2025-03-15T19:50:28Z
dc.date.available 2025-09-17T14:28:33Z
dc.date.issued 2025
dc.description.abstract Background:Alzheimer disease is a progressive neurological disorder marked by irreversible memory loss and cognitive decline. Traditional diagnostic tools, such as intracranial volume assessments, electroencephalography (EEG) signals, and brain magnetic resonance imaging (MRI), have shown utility in detecting the disease. However, artificial intelligence (AI) offers promise for automating this process, potentially enhancing diagnostic accuracy and accessibility.Methods:In this study, various machine learning models were used to detect Alzheimer disease, including K-nearest neighbor regression, support vector machines (SVM), AdaBoost regression, and logistic regression. A neural network was constructed and validated using data from 150 participants in the University of Washington's Alzheimer's Disease Research Center (Open Access Imaging Studies Series [OASIS] dataset). Cross-validation was also performed on the Alzheimer Disease Neuroimaging Initiative (ADNI) dataset to assess the robustness of the models.Results:Among the models tested, K-nearest neighbor regression achieved the highest accuracy, reaching 97.33%. The cross-validation on the ADNI dataset further confirmed the effectiveness of the models, demonstrating satisfactory results in screening and diagnosing Alzheimer disease in a community-based sample.Conclusion:The findings indicate that AI-based models, particularly K-nearest neighbor regression, provide promising accuracy for the early detection of Alzheimer disease. This approach has potential for further development into practical diagnostic tools that could be applied in clinical and community settings. en_US
dc.identifier.doi 10.1097/JCMA.0000000000001188
dc.identifier.issn 1726-4901
dc.identifier.issn 1728-7731
dc.identifier.scopus 2-s2.0-85218718005
dc.identifier.uri https://doi.org/10.1097/JCMA.0000000000001188
dc.identifier.uri https://hdl.handle.net/20.500.12514/9669
dc.language.iso en en_US
dc.publisher Lippincott Williams & Wilkins en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.subject Artificial Intelligence en_US
dc.subject Alzheimer Disease en_US
dc.subject Machine Learning en_US
dc.subject Magnetic Resonance Imaging en_US
dc.subject Neural Network en_US
dc.title Comparison of Machine Learning Algorithms for Automatic Prediction of Alzheimer Disease en_US
dc.type Article en_US
dspace.entity.type Publication

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