Early Diagnosis of Alzheimer’s Disease using Unsupervised Clustering
Farouk, Y., Rady, S. (2020). Early Diagnosis of Alzheimer’s Disease using Unsupervised Clustering. EKB Journal Management System, 20(2), 112-124. doi: 10.21608/ijicis.2021.51180.1044
Yasmeen Farouk; Sherine Rady. "Early Diagnosis of Alzheimer’s Disease using Unsupervised Clustering". EKB Journal Management System, 20, 2, 2020, 112-124. doi: 10.21608/ijicis.2021.51180.1044
Farouk, Y., Rady, S. (2020). 'Early Diagnosis of Alzheimer’s Disease using Unsupervised Clustering', EKB Journal Management System, 20(2), pp. 112-124. doi: 10.21608/ijicis.2021.51180.1044
Farouk, Y., Rady, S. Early Diagnosis of Alzheimer’s Disease using Unsupervised Clustering. EKB Journal Management System, 2020; 20(2): 112-124. doi: 10.21608/ijicis.2021.51180.1044

Early Diagnosis of Alzheimer’s Disease using Unsupervised Clustering

International Journal of Intelligent Computing and Information Sciences
Article 15, Volume 20, Issue 2, December 2020, Page 112-124  XML PDF (650.9 K)
Document Type: Original Article
DOI: 10.21608/ijicis.2021.51180.1044
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Authors
Yasmeen Farouk email 1; Sherine Radyorcid 2
1Information Systems Departement, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt.
2Ain Shams University
Abstract
Alzheimer's disease (AD) is a progressive brain disorder and a very common form of dementia. Neuroimaging techniques, such as Magnetic Resonance Imaging (MRI), produce detailed 3-dimensional images of the brain showing insights for amyloid deposits and inflammatory alterations as disease markers. The early diagnosis of AD using MRI provides a good chance for patients to prevent further brain deterioration by stopping the loss of nerve cells. This paper explores the use of unsupervised clustering approaches for the early diagnosis of AD. Though it is very common to use classification techniques for identifying medical diseases, the lack or the inaccuracies of labeled data can generate a problem. In this work, the k-means and k-medoids are compared while employing the Voxel Based Morphometry (VBM) features extracted from the MRI images. The effect of choosing certain local regions of interest (ROIs) for the analysis is also compared to the global whole-brain analysis. The results show that the proposed approach can perform an early diagnosis of AD with an accuracy of 76%.
Keywords
Unsupervised Learning; Clustering; Regions of Interest (ROI); Alzheimer’s disease; Magnetic Resonance Imaging (MRI)
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