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dc.contributor.authorAlashwal, Hany
dc.contributor.authorEl Halaby, Mohamed
dc.contributor.authorCrouse, Jacob
dc.contributor.authorAbdalla, Areeg
dc.contributor.authorMoustafa, Ahmed
dc.date.accessioned2020-01-13T03:01:19Z
dc.date.available2020-01-13T03:01:19Z
dc.date.issued2019
dc.identifier.citationAlashwal H., El Halaby, M., Crouse, J. J., Abdalla, A., & Moustafa A. A. (2019). The application of unsupervised clustering methods to Alzheimer's disease. Frontiers in Computational Neuroscience.en_AU
dc.identifier.urihttps://hdl.handle.net/2123/21653
dc.description.abstractClustering 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.en_AU
dc.language.isoen_USen_AU
dc.publisherFrontiers Mediaen_AU
dc.relationN/Aen_AU
dc.rightsCopyright © 2019 Alashwal, El Halaby, Crouse, Abdalla and Moustafa. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.en_AU
dc.subjectcomputational neuroscienceen_AU
dc.subjectdata-drivenen_AU
dc.subjectcluster analysisen_AU
dc.subjectalzheimer's diseaseen_AU
dc.subjectneurodegenerationen_AU
dc.subjectneurodegenerativeen_AU
dc.subjectbrainen_AU
dc.subjectmachine learningen_AU
dc.subjectneurologyen_AU
dc.titleThe Application of Unsupervised Clustering Methods to Alzheimer’s Diseaseen_AU
dc.typeArticleen_AU
dc.subject.asrccomputational neuroscienceen_AU
dc.subject.asrcFoR::110904 - Neurology and Neuromuscular Diseasesen_AU
dc.subject.asrcFoR::110999 - Neurosciences not elsewhere classifieden_AU
dc.identifier.doi10.3389/fncom.2019.00031
dc.type.pubtypePublisher versionen_AU


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