The application of unsupervised clustering methods to Alzheimer’s disease

Hany Alashwal*, Mohamed El Halaby, Jacob J. Crouse, Areeg Abdalla, Ahmed A. Moustafa

*Corresponding author for this work

Research output: Contribution to journalReview articleResearchpeer-review

38 Citations (Scopus)
2 Downloads (Pure)

Abstract

Clustering is a powerful machine learning tool for detecting structures in datasets. In the medical field, clustering has been proven to be a powerful tool for discovering patterns and structure in labeled and unlabeled datasets. Unlike supervised methods, clustering is an unsupervised method that works on datasets in which there is no outcome (target) variable nor is anything known about the relationship between the observations, that is, unlabeled data. In this paper, we focus on studying and reviewing clustering methods that have been applied to datasets of neurological diseases, especially Alzheimer’s disease (AD). The aim is to provide insights into which clustering technique is more suitable for partitioning patients of AD based on their similarity. This is important as clustering algorithms can find patterns across patients that are difficult for medical practitioners to find. We further discuss the implications of the use of clustering algorithms in the treatment of AD. We found that clustering analysis can point to several features that underlie the conversion from early-stage AD to advanced AD. Furthermore, future work can apply semi-clustering algorithms on AD datasets, which will enhance clusters by including additional information.

Original languageEnglish
Article number31
JournalFrontiers in Computational Neuroscience
Volume13
DOIs
Publication statusPublished - 24 May 2019
Externally publishedYes

Fingerprint

Dive into the research topics of 'The application of unsupervised clustering methods to Alzheimer’s disease'. Together they form a unique fingerprint.

Cite this