Classifying Alzheimer’s Disease with Machine Learning via Wavelet Transform Subband Combinations


  • Aalok Bhattacharya Staples High School
  • Xiangyi Cheng Staples High School



wavelet, subband, Mild Cognitive Impairment, Alzheimer's Disease, classification, screening


The 3-D wavelet transform has been used with machine learning techniques to help identify Alzheimer’s Disease (AD) and Mild Cognitive Impairment (MCI) through magnetic resonance imaging (MRI). Although this approach resulted in high accuracy, a large number of subband permutations were obtained due to the usage of the 3-D wavelet transform, which causes redundancy and computational inefficiency during classification. To address this issue, this study discovers the combination with the minimum subbands giving a comparable accuracy with the original approach that used all subbands. The Alzheimer’s Disease Neuroimaging Initiative (ADNI) was used to gain de-identified MRI scans for classification with 75% training and 25% test scans. The scans were standardized by pre-processing through skull stripping, segmentation, and smoothing using the MATLAB toolboxes Statistical Parametric Mapping 12 (SPM 12) and Computation Anatomy Toolbox 12 (CAT 12). Feature extraction was then completed through different 3-D wavelet transform subband combinations. Support vector machine (SVM) classification was used with radial basis function kernels (RBF) to screen patients for AD, MCI, or cognitively normal (CN) status 3 times per subband combination. The mean and standard deviation of the test accuracy for each combination were recorded. Among the tested combinations, the maximum mean test accuracy was 95.3%. To prevent overfitting, five-fold cross-validation was performed on the top 40% subband combinations based on the mean test accuracy. The maximum cross-validation accuracy is 97.3%. Therefore, this study shows the potential of using less 3-D wavelet transform subbands to help screen patients for AD or MCI in the future.


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Author Biography

Xiangyi Cheng, Staples High School


References or Bibliography

ADNI | About. (2009).

Ayaz, A., Ahmad, M. Z., Khurshid, K., & Kamboh, A. M. (2017). MRI based automated diagnosis of Alzheimer’s: Fusing 3D wavelet-features with clinical data. 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

Apostolova, L. G., Steiner, C. A., Akopyan, G. G., Dutton, R. A., Hayashi, K. M., Toga, A. W., Cummings, J. L., & Thompson, P. M. (2007). Three-Dimensional Gray Matter Atrophy Mapping in Mild Cognitive Impairment and Mild Alzheimer Disease. Archives of Neurology, 64(10), 1489.

Bhasin, H., & Agrawal, R. K. (2020). A combination of 3-D discrete wavelet transform and 3-D local binary pattern for classification of mild cognitive impairment. BMC Medical Informatics and Decision Making, 20(1).

Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.

Facts and Figures. (2021). Alzheimer’s Disease and Dementia.

Friston, K. J., Holmes, A. P., Worsley, K. J., Poline, J.-P. ., Frith, C. D., & Frackowiak, R. S. J. (1994). Statistical parametric maps in functional imaging: A general linear approach. Human Brain Mapping, 2(4), 189–210.

Henderson, V. (n.d.). Mild Cognitive Impairment. Retrieved February 15, 2022, from

Jha, D., Kim, J.-I., & Kwon, G.-R. (2017). Diagnosis of Alzheimer’s Disease Using Dual-Tree Complex Wavelet Transform, PCA, and Feed-Forward Neural Network. Journal of Healthcare Engineering, 2017, 1–13.

Kai, S., Li, K., & Selesnick, I. (n.d.). Wavelet Software at Brooklyn Poly. Retrieved February 8, 2022, from

Leifer, B. P. (2003). Early diagnosis of Alzheimer’s disease: clinical and economic benefits. Journal of the American Geriatrics Society, 51(5 Suppl Dementia), S281-288.

Mallat, S. G. (1989). A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7), 674–693.

Mishra, S. K., & Deepthi, V. H. (2020). Brain image classification by the combination of different wavelet transforms and support vector machine classification. Journal of Ambient Intelligence and Humanized Computing, 12(6), 6741–6749.

National Institute on Aging. (2019, May 22). Alzheimer’s Disease Fact Sheet. National Institute on Aging.

Rajapakse, J. C., Giedd, J. N., & Rapoport, J. L. (1997). Statistical approach to segmentation of single-channel cerebral MR images. IEEE Transactions on Medical Imaging, 16(2), 176–186.

Sujatha Kumari, B. A., Yadiyala, A. G. V., Aruna, B. J., Radha, C., & Shwetha, B. (2021). Early Detection of Mild Cognitive Impairment Using 3D Wavelet Transform. Data Intelligence and Cognitive Informatics, 445–455.

Tanveer, M., Richhariya, B., Khan, R. U., Rashid, A. H., Khanna, P., Prasad, M., & Lin, C. T. (2020). Machine Learning Techniques for the Diagnosis of Alzheimer’s Disease. ACM Transactions on Multimedia Computing, Communications, and Applications, 16(1s), 1–35.

The Dementias: Hope Through Research | National Institute of Neurological Disorders and Stroke. (n.d.). Retrieved January 29, 2022, from

Wang, X., Nan, B., Zhu, J., & Koeppe, R. (2014). Regularized 3D functional regression for brain image data via Haar wavelets. The Annals of Applied Statistics, 8(2).

Ye, D. H., Pohl, K. M., & Davatzikos, C. (2011). Semi-supervised Pattern Classification: Application to Structural MRI of Alzheimer’s Disease. 2011 International Workshop on Pattern Recognition in NeuroImaging, 1–4.

Zhang, Y., Dong, Z., Phillips, P., Wang, S., Ji, G., Yang, J., & Yuan, T.-F. (2015). Detection of subjects and brain regions related to Alzheimer’s disease using 3D MRI scans based on eigenbrain and machine learning. Frontiers in Computational Neuroscience, 9.



How to Cite

Bhattacharya, A., & Cheng, X. (2022). Classifying Alzheimer’s Disease with Machine Learning via Wavelet Transform Subband Combinations. Journal of Student Research, 11(2).



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