International Journal of Education and Management Engineering (IJEME)
IJEME Vol. 16, No. 3, 8 Jun. 2026
Cover page and Table of Contents: PDF (size: 1449KB)
Smart Diagnosis: An Ensemble Machine Learning Web Application for Early Detection of Alzheimer’s Disease
PDF (1449KB), PP.62-80
Views: 0 Downloads: 0
Author(s)
Yetunde D. Otun
1,*
Abosede O. Oguntunde
1
Samson A. Arekete
1
Oluwole B. Olajide
2
Benjamin S. Aribisala
3
Index Terms
Alzheimer’s Disease, Clinical Dementia Rating, Machine Learning, SMOTE Analysis, Web Application
Abstract
Alzheimer disease is a chronic neurodegenerative disorder and the primary cause of dementia among the population, which has a huge burden to the patients, their caregivers and the health care system. Timely intervention is necessary to reduce disease progression, facilitate timely intervention and improve the quality of life. But the traditional forms of diagnostic are frequently costly and non-available especially in resource-deficient environments. The research paper proposes an interpretable and cost-efficient machine-learning model that can be used to identify the presence of Alzheimer disease at its early stages based on clinical and demographic metrics based on the Open Access Series of Imaging Studies cross-sectional dataset, which contains 436 participants. The data consists of seven numeric and two categorical variables, whereas the Clinical Dementia Rating was changed into two categories namely demented and non-demented. An extensive preprocessing pipeline was used, which entailed missing value imputation, categorical encoding and elimination of irrelevant variables, as well as class balancing with the Synthetic Minority Oversampling Technique. A number of machine learning models were tested, which comprise Logistic Regression, Support Vector Machine, Random Forest, Gradient Boosting, and Extreme Gradient Boosting. The results show that the highest accuracy of 92% was attained using the model implemented by the ensemble and the tree, with the most accuracy being returned by the Random Forest and the ensemble model. Random Forest, too, had a sensitivity of 95%, whereas Gradient Boosting and Extreme Gradient Boosting had the highest area under the receiver operating characteristic curve of 98%. The models were implemented as a lightweight web application on the Flask framework, which can make real-time predictions and color coded. The system illustrates the possibility of combining interpretable machine learning with web technologies to make it possible to conduct easy and effective early screening of Alzheimer disease under resource-limited healthcare conditions.
Cite This Paper
Yetunde D. Otun, Abosede O. Oguntunde, Samson A. Arekete, Oluwole B. Olajide, Benjamin S. Aribisala, "Smart Diagnosis: An Ensemble Machine Learning Web Application for Early Detection of Alzheimer’s Disease", International Journal of Education and Management Engineering (IJEME), Vol.16, No.3, pp. 62-80, 2026. DOI:10.5815/ijeme.2026.03.05
Reference
[1]H. T. Khan, K. M. Addo, and H. Findlay, “Public health challenges and responses to the growing ageing populations,” Public Health Challenges, vol. 3, no. 3, e213, 2024. doi:10.1002/puh2.213.
[2]R. K. Chaudhary, U. V. Mateti, P. Khanal, K. B. Rawal, P. Jain, V. S. Patil, et al., “Alzheimer’s disease: Epidemiology, neuropathology, and neurochemistry,” in Computational and Experimental Studies in Alzheimer’s Disease, CRC Press, pp. 1–14, 2024.
[3]Alzheimer’s Association, “2019 Alzheimer’s disease facts and figures,” Alzheimer’s & Dementia, vol. 15, no. 3, pp. 321–387, 2019. doi:10.1016/j.jalz.2019.01.010.
[4]H. Hampel, R. Au, S. Mattke, W. M. van der Flier, P. Aisen, L. Apostolova, et al., “Designing the next-generation clinical care pathway for Alzheimer’s disease,” Nat. Aging, vol. 2, no. 8, pp. 692–703, 2022. doi:10.1038/s43587-022-00269-x.
[5]A. G. Shubar, K. Ramakrishnan, and C. K. Ho, “Optimizing machine learning models for accessible early cognitive impairment prediction: A novel cost-effective model selection algorithm,” IEEE Access, 2024. doi:10.1109/ACCESS.2024.10763483.
[6]E. Barbierato and A. Gatti, “The challenges of machine learning: A critical review,” Electronics, vol. 13, no. 2, p. 2, 2024. doi:10.3390/electronics13020416.
[7]J. Mateo, J. M. Rius-Peris, A. I. Maraña-Pérez, A. Valiente-Armero, and A. M. Torres, “Extreme gradient boosting machine learning method for predicting medical treatment in patients with acute bronchiolitis,” Biocybern. Biomed. Eng., vol. 41, no. 2, pp. 792–801, 2021. doi:10.1016/j.bbe.2021.04.015.
[8]S. Ng, S. Masarone, D. Watson, and M. R. Barnes, “The benefits and pitfalls of machine learning for biomarker discovery,” Cell Tissue Res., vol. 394, no. 1, pp. 17–31, 2023. doi:10.1007/s00441-023-03816-z.
[9]S. Hanke, F. Mangialasche, M. Bödenler, B. Neumayer, T. Ngandu, P. Mecocci, et al., “AI-based predictive modelling of the onset and progression of dementia,” Smart Cities, vol. 5, no. 2, pp. 700–714, 2022. doi:10.3390/smartcities5020036.
[10]L. Bloch, C. M. Friedrich, and Alzheimer’s Disease Neuroimaging Initiative, “Machine learning workflow to explain black-box models for early Alzheimer’s disease classification evaluated for multiple datasets,” SN Comput. Sci., vol. 3, no. 6, p. 509, 2022. doi:10.1007/s42979-022-01371-y.
[11]V. Vimbi, N. Shaffi, and M. Mahmud, “Interpreting artificial intelligence models: A systematic review on the application of LIME and SHAP in Alzheimer’s disease detection,” Brain Informatics, vol. 11, no. 1, p. 10, 2024. doi:10.1186/s40708-024-00222-1.
[12]M. Li, Y. Jiang, Y. Zhang, and H. Zhu, “Medical image analysis using deep learning algorithms,” Front. Public Health, vol. 11, 1273253, 2023. doi:10.3389/fpubh.2023.1273253.
[13]S. Klöppel, C. M. Stonnington, C. Chu, B. Draganski, R. I. Scahill, J. D. Rohrer, N. C. Fox, C. R. Jack, J. Ashburner, and R. S. J. Frackowiak, “Automatic classification of MR scans in Alzheimer’s disease,” Brain, vol. 131, no. 3, pp. 681–689, 2008. doi:10.1093/brain/awm319.
[14]E. Moradi, A. Pepe, C. Gaser, H. Huttunen, and J. Tohka, “Machine learning framework for early MRI-based Alzheimer’s conversion prediction in MCI subjects,” NeuroImage, vol. 104, pp. 398–412, 2015. doi:10.1016/j.neuroimage.2014.10.002.
[15]Z. Xiao, Y. Ding, T. Lan, C. Zhang, C. Luo, and Z. Qin, “Brain MR image classification for Alzheimer’s disease diagnosis based on multifeatured fusion,” Comput. Math. Methods Med., vol. 2017, Article 1952373, 2017. doi:10.1155/2017/1952373.
[16]P. Baglat, A. W. Salehi, A. Gupta, and G. Gupta, “Multiple machine learning models for detection of Alzheimer’s disease using OASIS dataset,” in Proc. Int. Working Conf. Transfer and Diffusion of IT, Springer, pp. 614–622, 2020. doi:10.1007/978-3-030-64849-7_54.
[17]R. Caldas, N. Costa, D. A. Santos, M. Bessa, and A. Sousa, “Alzheimer’s disease detection using inertial measurement units and machine learning algorithms,” IEEE Access, vol. 9, pp. 134483–134494, 2021. doi:10.1109/ACCESS.2021.3115396.
[18]L. Yue, W.-g. Chen, S.-c. Liu, S.-b. Chen, and S.-f. Xiao, “An explainable machine learning based prediction model for Alzheimer’s disease in China longitudinal aging study,” Front. Aging Neurosci., vol. 15, 1267020, 2023. doi:10.3389/fnagi.2023.1267020.
[19]H. Alshamlan, A. Alwassel, A. Banafa, and L. Alsaleem, “Improving Alzheimer’s disease prediction with different machine learning approaches and feature selection techniques,” Diagnostics, vol. 14, no. 19, p. 2237, 2024. doi:10.3390/diagnostics14192237.
[20]K. Van Steen, C. M. Van Duijn, V. Escott-Price, et al., “Machine learning in Alzheimer’s disease genetics,” Nature Communications, vol. 16, p. 6726, 2025.
[21]R. Govindarajan, K. Thirunadanasikamani, K. K. Napa, S. Sathya, J. Senthil Murugan, and K. G. Chandi Priya, “Development of an explainable machine learning model for Alzheimer’s disease prediction using clinical and behavioural features,” MethodsX, vol. 15, p. 103491, 2025.
[22]E. Aslan and Y. Özüpak, “Comparison of machine learning algorithms for automatic prediction of Alzheimer disease,” Journal of Chinese Medical Association, vol. 88, no. 2, pp. 98–107, 2025.