ML-Driven Alzheimer’s disease prediction: A deep ensemble modeling approach
Alzheimer’s disease (AD) is a progressive neurological disorder characterized by cognitive decline due to brain cell death, typically manifesting later in life.Early and accurate detection is critical for effective disease management and treatment. This study proposes an ensemble learning framework...
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| Language: | English |
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Elsevier
2025-06-01
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| Series: | SLAS Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2472630325000561 |
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| author | Mustafa Lateef Fadhil Jumaili Emrullah Sonuç |
| author_facet | Mustafa Lateef Fadhil Jumaili Emrullah Sonuç |
| author_sort | Mustafa Lateef Fadhil Jumaili |
| collection | DOAJ |
| description | Alzheimer’s disease (AD) is a progressive neurological disorder characterized by cognitive decline due to brain cell death, typically manifesting later in life.Early and accurate detection is critical for effective disease management and treatment. This study proposes an ensemble learning framework that combines five deep learning architectures (VGG16, VGG19, ResNet50, InceptionV3, and EfficientNetB7) to improve the accuracy of AD diagnosis. We use a comprehensive dataset of 3,714 MRI brain scans collected from specialized clinics in Iraq, categorized into three classes: NonDemented (834 images), MildDemented (1,824 images), and VeryDemented (1,056 images). The proposed voting ensemble model achieves a diagnostic accuracy of 99.32% on our dataset. The effectiveness of the model is further validated on two external datasets: OASIS (achieving 86.6% accuracy) and ADNI (achieving 99.5% accuracy), demonstrating competitive performance compared to existing approaches. Moreover, the proposed model exhibits high precision and recall across all stages of dementia, providing a reliable and robust tool for early AD detection. This study highlights the effectiveness of ensemble learning in AD diagnosis and shows promise for clinical applications. |
| format | Article |
| id | doaj-art-e33c388a424343d399196622237965a1 |
| institution | OA Journals |
| issn | 2472-6303 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | SLAS Technology |
| spelling | doaj-art-e33c388a424343d399196622237965a12025-08-20T02:32:19ZengElsevierSLAS Technology2472-63032025-06-013210029810.1016/j.slast.2025.100298ML-Driven Alzheimer’s disease prediction: A deep ensemble modeling approachMustafa Lateef Fadhil Jumaili0Emrullah Sonuç1Department of Computer Engineering, Karabuk University, Karabük, 78050, Türkiye; Department of Computer Science, College of Computer Science and Mathematics, Tikrit University, Tikrit, 34001, IraqDepartment of Computer Engineering, Karabuk University, Karabük, 78050, Türkiye; Corresponding author.Alzheimer’s disease (AD) is a progressive neurological disorder characterized by cognitive decline due to brain cell death, typically manifesting later in life.Early and accurate detection is critical for effective disease management and treatment. This study proposes an ensemble learning framework that combines five deep learning architectures (VGG16, VGG19, ResNet50, InceptionV3, and EfficientNetB7) to improve the accuracy of AD diagnosis. We use a comprehensive dataset of 3,714 MRI brain scans collected from specialized clinics in Iraq, categorized into three classes: NonDemented (834 images), MildDemented (1,824 images), and VeryDemented (1,056 images). The proposed voting ensemble model achieves a diagnostic accuracy of 99.32% on our dataset. The effectiveness of the model is further validated on two external datasets: OASIS (achieving 86.6% accuracy) and ADNI (achieving 99.5% accuracy), demonstrating competitive performance compared to existing approaches. Moreover, the proposed model exhibits high precision and recall across all stages of dementia, providing a reliable and robust tool for early AD detection. This study highlights the effectiveness of ensemble learning in AD diagnosis and shows promise for clinical applications.http://www.sciencedirect.com/science/article/pii/S2472630325000561Alzheimer’s diseaseDeep learningEnsemble modelsMedical image classificationMRI |
| spellingShingle | Mustafa Lateef Fadhil Jumaili Emrullah Sonuç ML-Driven Alzheimer’s disease prediction: A deep ensemble modeling approach SLAS Technology Alzheimer’s disease Deep learning Ensemble models Medical image classification MRI |
| title | ML-Driven Alzheimer’s disease prediction: A deep ensemble modeling approach |
| title_full | ML-Driven Alzheimer’s disease prediction: A deep ensemble modeling approach |
| title_fullStr | ML-Driven Alzheimer’s disease prediction: A deep ensemble modeling approach |
| title_full_unstemmed | ML-Driven Alzheimer’s disease prediction: A deep ensemble modeling approach |
| title_short | ML-Driven Alzheimer’s disease prediction: A deep ensemble modeling approach |
| title_sort | ml driven alzheimer s disease prediction a deep ensemble modeling approach |
| topic | Alzheimer’s disease Deep learning Ensemble models Medical image classification MRI |
| url | http://www.sciencedirect.com/science/article/pii/S2472630325000561 |
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