Recent Research Reviews Journal
IRO Journals
Volume 4 — Issue 1 — June 2025

A Comprehensive Survey on Classification and Prediction Techniques for Alzheimer’s Disease

https://doi.org/10.36548/rrrj.2025.1.005
Maga Vigna C.
Department of Computer Science and Engineering, Thiagarajar College of Engineering, Madurai, India
Kavitha Devi M. K.
Department of Applied Mathematics and Computational Science, Thiagarajar College of Engineering, Madurai, India
Vaira Suganthi G.
Department of Computer Science and Design, Sethu Institute of Technology, Kariapatti, India
PDF
PDF

How to Cite

C., Maga Vigna, Kavitha Devi M. K., and Vaira Suganthi G. 2025. “A Comprehensive Survey on Classification and Prediction Techniques for Alzheimer’s Disease”. Recent Research Reviews Journal 4 (1): 72-97. https://doi.org/10.36548/rrrj.2025.1.005.
ACM ACS APA ABNT Chicago Harvard IEEE MLA Turabian Vancouver AMA

Download Citation

Endnote/Zotero/Mendeley (RIS) BibTeX

Keywords

— Alzheimer’s Disease
— Dementia
— Mild Cognitive Impairment (MCI)
— Machine Learning
— Deep Learning
— Convolutional Neural Networks
— Multitask Learning
— Alzheimer’s Progression
— AI in Healthcare
— Neurodegenerative Disorders
Published: 20-05-2025

Abstract

Alzheimer's disease (AD) is one of many disorders that affect the brain. It is thought to have a number of neurological and physical causes, including the loss of muscle memory. It is characterized by an aggregation of clinical features; memory loss, confusion, and personality changes tend to worsen over time. The WHO stated that in 2021 there were more than 55 million people living with dementia, and this figure is estimated to increase to as high as 139 million by the year 2050. Alzheimer’s is responsible for 60-70% of dementia cases, with many other factors considered. Mild cognitive impairment (MCI) is a condition where individuals experience significant challenges in memory, cognition, and decision-making, often influenced by age and educational background. Individuals with MCI have two to three times the chance of advancing to Alzheimer's dementia compared to older adults, with an estimated annual transition rate of 3% to 15%. Various strategies across a range of AI tools, including machine learning and deep learning, are used for for AD diagnosis, particularly the application of machine learning algorithms. This paper presents the results of various research studies conducted in the last few years that particularly concern the path from MCI to AD. It focuses on reviewing machine learning and deep learning algorithms, covering convolutional neural networks and multitask learning techniques to predict Alzheimer's progression.

References

  1. Gao, Shuangshuang, and Dimas Lima. "A review of the application of deep learning in the detection of Alzheimer's disease." International Journal of Cognitive Computing in Engineering 3 (2022): 1-8.
  2. Chamakuri, Radhakrishna, and Hyma Janapana. "A systematic review on recent methods on deep learning for automatic detection of alzheimer’s disease." Medicine in Novel Technology and Devices (2024): 100343.
  3. Shanmugavadivel, Kogilavani, V. E. Sathishkumar, Jaehyuk Cho, and Malliga Subramanian. "Advancements in computer-assisted diagnosis of Alzheimer's disease: A comprehensive survey of neuroimaging methods and AI techniques for early detection." Ageing Research Reviews 91 (2023): 102072.
  4. Cao, Eric, Da Ma, Siddharth Nayak, and Tim Q. Duong. "Deep learning combining FDG-PET and neurocognitive data accurately predicts MCI conversion to Alzheimer's dementia 3-year post MCI diagnosis." Neurobiology of Disease 187 (2023): 106310.
  5. Balaji, Prasanalakshmi, Mousmi Ajay Chaurasia, Syeda Meraj Bilfaqih, Anandhavalli Muniasamy, and Linda Elzubir Gasm Alsid. "Hybridized deep learning approach for detecting Alzheimer’s disease." Biomedicines 11, no. 1 (2023): 149.
  6. Leela, M., K. Helenprabha, and L. Sharmila. "Prediction and classification of Alzheimer disease categories using integrated deep transfer learning approach." Measurement: Sensors 27 (2023): 100749.
  7. Khatun, Maleka, Md Manowarul Islam, Habibur Rahman Rifat, Md Shamim Bin Shahid, Md Alamin Talukder, and Md Ashraf Uddin. "Hybridized convolutional neural networks and long short-term memory for improved Alzheimer’s disease diagnosis from MRI scans." In 2023 26th International Conference on Computer and Information Technology (ICCIT), IEEE (2023): 1-6.
  8. Murugan, Suriya, Chandran Venkatesan, M. G. Sumithra, Xiao-Zhi Gao, B. Elakkiya, Muthuramalingam Akila, and S. Manoharan. "DEMNET: A deep learning model for early diagnosis of Alzheimer diseases and dementia from MR images." Ieee Access 9 (2021): 90319-90329.
  9. Gunawardena, K. A. N. N. P., R. N. Rajapakse, and Nihal D. Kodikara. "Applying convolutional neural networks for pre-detection of alzheimer's disease from structural MRI data." In 2017 24th international conference on mechatronics and machine vision in practice (M2VIP), IEEE (2017): 1-7.
  10. Yang, Zhiguang, and Zhaoyu Liu. "The risk prediction of Alzheimer’s disease based on the deep learning model of brain 18F-FDG positron emission tomography." Saudi Journal of Biological Sciences 27, no. 2 (2020): 659-665.
  11. Hassan, Najmul, Abu Saleh Musa Miah, Kota Suzuki, Yuichi Okuyama, and Jungpil Shin. "Stacked CNN-based multichannel attention networks for Alzheimer disease detection." Scientific Reports 15, no. 1 (2025): 5815.
  12. Fedorov, Alex, R. Devon Hjelm, Anees Abrol, Zening Fu, Yuhui Du, Sergey Plis, and Vince D. Calhoun. "Prediction of progression to Alzheimer's disease with deep infomax." In 2019 IEEE EMBS International conference on biomedical & health informatics (BHI), IEEE (2019): 1-5.
  13. Asaduzzaman, Md, Md Khorshed Alom, and Md Ebtidaul Karim. "ALZENET: Deep Learning-Based Early Prediction of Alzheimer's Disease through Magnetic Resonance Imaging Analysis." Telematics and Informatics Reports (2025): 100189.
  14. García-Gutiérrez, Fernando, Laura Hernández-Lorenzo, María Nieves Cabrera-Martín, Jordi A. Matias-Guiu, José L. Ayala, and Alzheimer’s Disease Neuroimaging Initiative. "Predicting changes in brain metabolism and progression from mild cognitive impairment to dementia using multitask Deep Learning models and explainable AI." NeuroImage 297 (2024): 120695.
  15. Kiss, Gabor. "Early Detection of Alzheimer's Disease Using Artificial Intelligence." Procedia Computer Science 237 (2024): 485-492.
  16. Illan, I. A., J. M. Gorriz, J. Ramirez, R. Chaves, F. Segovia, M. López, D. Salas-Gonzalez, P. Padilla, and C. G. Puntonet. "Machine learning for very early Alzheimer's disease diagnosis; a 18 F-FDG and pib PET comparison." In IEEE Nuclear Science Symposuim & Medical Imaging Conference, IEEE (2010): 2334-2337.
  17. Olle, Daniel Georges Olle, Julien Zoobo Bisse, and Ghislain Abessolo Alo’o. "Application and comparison of K-means and PCA based segmentation models for Alzheimer disease detection using MRI." Discover Artificial Intelligence 4, no. 1 (2024): 11.
  18. Uysal, Gokce, and Mahmut Ozturk. “Classifying Early and Late Mild Cognitive Impairment Stages of Alzheimer's Disease by Analyzing Different Brain Areas.” IEEE Conference Paper, 2020
  19. Agostinho, Daniel, Marco Simões, and Miguel Castelo-Branco. “Predicting Conversion from Mild Cognitive Impairment to Alzheimer’s Disease: A Multimodal Approach.” Brain Communications 6, no. 4 (2024)
  20. Rajab, M.D., Jammeh, E., Taketa, T. et al. Assessment of Alzheimer-related pathologies of dementia using machine learning feature selection. Alz Res Therapy 15, 47 (2023).
  21. El-Geneedy, Marwa, Hossam El-Din Moustafa, Fahmi Khalifa, Hatem Khater, and Eman AbdElhalim. “An MRI-Based Deep Learning Approach for Accurate Detection of Alzheimer’s Disease.” Alexandria Engineering Journal 63 (2023): 211–221.
  22. Zhiheng Zhou, Qi Wang, Xiaoyu An, Siwei Chen, Yongan Sun, Guanghui Wang, Guiying Yan, A novel graph neural network method for Alzheimer’s disease classification,Computers in Biology and Medicine,Volume 180, 2024, 108869,I SSN 0010-4825.
  23. A. R. Zouaoui, Y. Brik, B. Attallah, M. Djeriuoi and M. Belkhelfa, "Transfer learning approach for Alzheimer’s disease diagnosis using MRI images," 2022 International Conference of Advanced Technology in Electronic and Electrical Engineering (ICATEEE), M'sila, Algeria (2022): 1-6
  24. Linfeng Liu, Siyu Liu, Lu Zhang, Xuan Vinh To, Fatima Nasrallah, Shekhar S. Chandra, “Cascaded Multi-Modal Mixing Transformers for Alzheimer’s Disease Classification with Incomplete Data”, NeuroImage, Volume 277, 2023, 120267, ISSN 1053-8119.
  25. Mahmud, Tanjim, Koushick Barua, Sultana Umme Habiba, Nahed Sharmen, Mohammad Shahadat Hossain, and Karl Andersson. 2024. "An Explainable AI Paradigm for Alzheimer’s Diagnosis Using Deep Transfer Learning" Diagnostics 14, no. 3: 345