A Hybrid Approach to Alzheimer’s Diagnosis: Predictive Modeling Using Diagnostic Data and MRI Images Integrated in a Web Interface

Open Access

Issue		EPJ Web Conf. Volume 343, 2025 1^st International Conference on Advances and Innovations in Mechanical, Aerospace, and Civil Engineering (AIMACE-2025)


Article Number		05009
Number of page(s)		19
Section		Artificial Intelligence & Machine Learning in Engineering
DOI		https://doi.org/10.1051/epjconf/202534305009
Published online		19 December 2025

J. Fessel, The several ways to authentically cure Alzheimer's dementia. Ageing Res. Rev. 92, 102093 (2023). https://doi.org/10.1016/i.arr.2023.102093 [Google Scholar]
P. Goel, S. Chakrabarti, K. Goel, K. Bhutani, T. Chopra, S. Bali, Neuronal cell death mechanisms in Alzheimer's disease: an insight. Front. Mol. Neurosci. 15, 937133 (2022). https://doi.org/10.3389/fnmol.2022.937133 [Google Scholar]
J. Therriault, et al., Biomarker modeling of Alzheimer's disease using PET-based Braak staging. Nat. Aging 2, 526–535 (2022). https://doi.org/10. 1038/s43587-022-00204-0 [Google Scholar]
H. Hampel, et al., The use of lumbar puncture and safety recommendations in Alzheimer's disease: a plain language summary. Neurodegener. Dis. Manag. 12, 221–229 (2022). https://doi.org/10.2217/nmt-2022-0012 [Google Scholar]
O. Hansson, et al., The Alzheimer's Association appropriate use recommendations for blood biomarkers in Alzheimer's disease. Alzheimer's Dement. 18, 2669–2686 (2022). https://doi.org/10.1002/alz.12756 [Google Scholar]
F.M. Feringa, R. Van Der Kant, Cholesterol and Alzheimer's disease: from risk genes to pathological effects. Front. Aging Neurosci. 13, 690372 (2021). https://doi.org/10.3389/fnagi.2021.690372 [Google Scholar]
S.-H. Han, K.W. Kim, S. Kim, Y.C. Youn, Artificial neural network: understanding the basic concepts without mathematics. Dement. Neurocogn. Disord. 17, 83–89 (2018). https://doi.org/10.12779/dnd.2018.17.3.83 [Google Scholar]
S. Ray, A quick review of machine learning algorithms. In: 2019 Int. Conf. Mach. Learn., Big Data, Cloud Parallel Comput. (COMITCon), 35–39 (2019). https://doi.org/10.1109/COMITCon.2019.8862451 [Google Scholar]
L. Alzubaidi, et al., Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J. Big Data 8, 53 (2021). https://doi.org/10.1186/s40537-021-00444-8 [CrossRef] [Google Scholar]
C. Goutte, E. Gaussier, A probabilistic interpretation of precision, recall and F-score, with implication for evaluation. In: Adv. Inf. Retr. 3408, 345–359 (2005). https://doi.org/10.1007/978-3-540-31865-1 25 [Google Scholar]
S.E. Sorour, A.A.A. El-Mageed, K.M. Albarrak, A.K. Alnaim, A.A. Wafa, E. El-Shafeiy, Classification of Alzheimer's disease using MRI data based on deep learning techniques. J. King Saud Univ. Comput. Inf. Sci. 36, 101940 (2024). https://doi.org/10.1016/i.iksuci.2024.101940 [Google Scholar]
A. Ebrahimi, S. Luo, Alzheimer's Disease Neuroimaging Initiative, Convolutional neural networks for Alzheimer's disease detection on MRI images. J. Med. Imaging 8, 024503 (2021). https://doi.org/10.1117/1.JMI.8.2.024503 [Google Scholar]
M. Heenaye-Mamode Khan, P. Reesaul, M.M. Auzine, A. Taylor, Detection of Alzheimer's disease using pre-trained deep learning models through transfer learning: a review. Artif. Intell. Rev. 57, 275 (2024). https://doi.org/10.1007/s10462-024- 10914-z [Google Scholar]
S. Sava§, Detecting the stages of Alzheimer's disease with pre-trained deep learning architectures. Arab. J. Sci. Eng. 47, 2201–2218 (2022). https://doi.Org/10.1007/s13369-021-06131-3 [Google Scholar]
Z. Kong, M. Zhang, W. Zhu, Y. Yi, T. Wang, B. Zhang, Multi-modal data Alzheimer's disease detection based on 3D convolution. Biomed. Signal Process. Control 75, 103565 (2022). https://doi.org/10.1016/j.bspc.2022.103565 [Google Scholar]

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