Review of Alzheimer’s Disease Classification Techniques


  • Aow Yong Li Yew Faculty of Computing, Universiti Teknologi Malaysia, 81318 Johor Bahru, Johor, Malaysia.
  • Ghazali Sulong Faculty of Computing, Universiti Teknologi Malaysia, 81318 Johor Bahru, Johor, Malaysia. School of Informatics and Applied Mathematics, Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu, Malaysia.


Alzheimer’s Disease, Feature Extraction, Feature Selection, Principal Component Analysis, Support Vector Machine,


Alzheimer’s disease is a brain degeneration illness. It requires earlier detection in order to improve the quality of life of the patient. Many researchers have studied different computed aided approaches in order to help in the diagnosis of the disease. However, there are few well-known techniques that were used in most of the studies. It is challenging in mild cognitive impairment classification too. Therefore, this paper will review the common used methods and the state-of-art methods in Alzheimer’s disease classification. This helps researchers to identify the suitable methodologies in different stages of classification.


Alzheimer’s Association, “2016 Alzheimer’s disease facts and figures,” Alzheimer’s Dement., vol. 12, no. 4, pp. 459–509, Apr. 2016.

Alzheimer’s Association, “What is Alzheimer’s,” 2017. [Online]. Available: [Accessed: 03-Jan-2017].

G. B. Frisoni, N.C. Fox, J. P. Scheltens, and P. M. Thompson, “The clinical use of structural MRI in Alzheimer disease,” Nat. Rev. Neurol, vol. 6, no. 2, pp. 67–77, 2010.

B. Duthey, “Background Paper 6.11 Alzheimer Disease and other Dementias,” 2013.

C. R. Munteanu, C. Fernandez-Lozano, V. Mato Abad, S. Pita Fernández, J. Álvarez-Linera, J. A. Hernández-Tamames, and A. Pazos, “Classification of mild cognitive impairment and Alzheimer’s Disease with machine-learning techniques using 1H Magnetic Resonance Spectroscopy data,” Expert Syst. Appl., vol. 42, no. 15–16, pp. 6205–6214, 2015.

M. Termenon, M. Graña, A. Besga, J. Echeveste, and A. GonzalezPinto, “Lattice independent component analysis feature selection on diffusion weighted imaging for Alzheimer’s disease classification,” Neurocomputing, vol. 114, pp. 132–141, Aug. 2013.

Y. Zhang, S. Wang, P. Phillips, Z. Dong, and G. Ji, “Detection of Alzheimer ’ s disease and mild cognitive impairment based on structural volumetric MR images using 3D-DWT and WTA-KSVM trained by PSOTVAC,” Biomed. Signal Process. Control, vol. 21, pp. 58–73, 2015.

L. Khedher, J. Ramírez, J. M. Górriz, A. Brahim, and F. Segovia, “Early diagnosis of Alzheimer â€TM s disease based on partial least squares , principal component analysis and support vector machine using segmented MRI images,” Neurocomputing, vol. 151, pp. 139– 150, 2015.

Y. Zhang, S. Wang, and Z. Dong, “Classification of Alzheimer Disease Based on Structural Magnetic Resonance Imaging by Kernel Support Vector Machine Decision Tree,” Prog. Electromagn. Res., vol. 144, pp. 171–184, 2014.

C. Salvatore, A. Cerasa, P. Battista, M. C. Gilardi, A. Quattrone, and I. Castiglioni, “Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer ’ s disease : a machine learning approach,” Front. Neurosci., vol. 9, no. September, pp. 1–13, 2015.

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, Jan. 2015.

L. G. Brooks and D. A. Loewenstein, “Assessing the progression of mild cognitive impairment to Alzheimer’s disease: current trends and future directions.,” Alzheimers. Res. Ther., vol. 2, no. 5, p. 28, 2010.

N. Philippi, V. Noblet, E. Duron, B. Cretin, C. Boully, I. Wisniewski, M. L. Seux, C. Martin-Hunyadi, E. Chaussade, C. Demuynck, S. Kremer, S. Lehéricy, D. Gounot, J. P. Armspach, O. Hanon, and F. Blanc, “Exploring anterograde memory: a volumetric MRI study in patients with mild cognitive impairment,” Alzheimers. Res. Ther., vol. 8, no. 1, p. 26, 2016.

Y. Wang, Y. Fan, P. Bhatt, and C. Davatzikos, “High-dimensional pattern regression using machine learning: from medical images to continuous clinical variables.,” Neuroimage, vol. 50, no. 4, pp. 1519– 35, May 2010.

M. M. Dessouky, M. A. Elrashidy, T. E. Taha, and H. M. Abdelkader, “Selecting and extracting effective features for automated diagnosis of Alzheimer’s disease,” Int. J. Comput. Appl., vol. 81, no. 4, pp. 17–28, 2013.

H. Park, J. ju Yang, J. Seo, and J. min Lee, “Dimensionality reduced cortical features and their use in predicting longitudinal changes in Alzheimer’s disease,” Neurosci. Lett., vol. 550, pp. 17–22, 2013.

L. J. P. Van Der Maaten, E. O. Postma, and H. J. Van Den Herik, “Dimensionality Reduction: A Comparative Review,” J. Mach. Learn. Res., vol. 10, pp. 1–41, 2009.

A. Farzan, S. Mashohor, A. R. Ramli, and R. Mahmud, “Boosting diagnosis accuracy of Alzheimer’s disease using High dimensional recognition of longitudinal brain atrophy patterns,” Behav. Brain Res., vol. 290, pp. 124–130, Apr. 2015.

M. M. Dessouky, M. A. Elrashidy, T. E. Taha, and H. M. Abdelkader, “Computer-aided diagnosis system for Alzheimer’s disease using different discrete transform techniques,” Am J Alzheimers Dis Other Demen, vol. 31, no. 3, pp. 282–293, 2016.

A. Hyvärinen, J. Karhunen, and E. Oja, Independent Component Analysis. 2001.

A. A. Willette, V. D. Calhoun, J. M. Egan, and D. Kapogiannis, “Prognostic classification of mild cognitive impairment and Alzheimer’s disease: MRI independent component analysis.,” Psychiatry Res., vol. 224, no. 2, pp. 81–8, Nov. 2014.

X. Liu, D. Tosun, M. W. Weiner, and N. Schuff, “Locally linear embedding (LLE) for MRI based Alzheimer’s disease classification,” Neuroimage, vol. 83, pp. 148–157, Dec. 2013.

A. Demirhan, T. M. Nir, A. Zavaliangos-petropulu, C. R. Jack, M. W. Weiner, M. A. Bernstein, P. M. Thompson, N. Jahanshad, C. Technology, and F. Technology, “Feature selection improves the accuracy of classifying alzheimer disease using diffusion tensor images,” in 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), 2015, pp. 126–130.

K. Kira and L. Rendell, “The feature selection problem: Traditional methods and a new algorithm,” Aaai, pp. 129–134, 1992.

D. Sarwinda and A. Bustamam, “Detection of Alzheimer’s disease using advanced local binary pattern from hippocampus and whole brain of MR images,” in 2016 International Joint Conference on Neural Networks (IJCNN), 2016, pp. 5051–5056.

L. Nanni, A. Lumini, and S. Brahnam, “Local binary patterns variants as texture descriptors for medical image analysis,” Artif. Intell. Med., vol. 49, no. 2, pp. 117–125, 2010.

I. Beheshti and H. Demirel, “Feature-ranking-based Alzheimer’s disease classification from structural MRI,” Magn. Reson. Imaging, vol. 34, no. 3, pp. 252–263, 2016.

R. Cuingnet, E. Gerardin, J. Tessieras, G. Auzias, S. Lehericy, M. O. Habert, M. Chupin, H. Benali, and O. Colliot, “Automatic classification of patients with Alzheimer’s disease from structural MRI: A comparison of ten methods using the ADNI database,” Neuroimage, vol. 56, no. 2, pp. 766–781, 2011.

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.

J. C. Platt, “Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines,” Adv. kernel methods, pp. 185– 208, 1998.

S. Ghosh and S. K. S. Dubey, “Comparative analysis of k-means and fuzzy c-means algorithms,” Ijacsa, vol. 4, no. 4, pp. 35–38, 2013.

C. Aguilar, E. Westman, J.-S. Muehlboeck, P. Mecocci, B. Vellas, M. Tsolaki, I. Kloszewska, H. Soininen, S. Lovestone, C. Spenger, A. Simmons, and L.-O. Wahlund, “Different multivariate techniques for automated classification of MRI data in Alzheimer’s disease and mild cognitive impairment.,” Psychiatry Res., vol. 212, no. 2, pp. 89–98, May 2013.

E. Westman, A. Simmons, Y. Zhang, J.-S. Muehlboeck, C. Tunnard, Y. Liu, L. Collins, A. Evans, P. Mecocci, B. Vellas, M. Tsolaki, I. Kłoszewska, H. Soininen, S. Lovestone, C. Spenger, and L.-O. Wahlund, “Multivariate analysis of MRI data for Alzheimer’s disease, mild cognitive impairment and healthy controls.,” Neuroimage, vol. 54, no. 2, pp. 1178–87, Jan. 2011.

A. L. Blum and P. Langley, “Selection of relevant features and examples in machine learning,” Artif. Intell., vol. 97, no. 1–2, pp. 245– 271, 1997.

J. Friedman, T. Hastie, and R. Tibshirani, “Regularization Paths for Generalized Linear Models via Coordinate Descent,” J. Stat. Softw., vol. 33, no. 1, pp. 1–11, 2010.

Y. Cho, J.-K. Seong, Y. Jeong, and S. Y. Shin, “Individual subject classification for Alzheimer’s disease based on incremental learning using a spatial frequency representation of cortical thickness data.,” Neuroimage, vol. 59, no. 3, pp. 2217–30, Feb. 2012.




How to Cite

Yew, A. Y. L., & Sulong, G. (2017). Review of Alzheimer’s Disease Classification Techniques. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(3-3), 117–123. Retrieved from