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dc.contributor.advisorBremdal, Bernt
dc.contributor.advisorHelge, Fredriksen
dc.contributor.advisorNymo, Ståle
dc.contributor.authorAsgari, Asal
dc.date.accessioned2023-11-06T08:03:03Z
dc.date.available2023-11-06T08:03:03Z
dc.date.issued2023-05-14en
dc.description.abstractThe purpose of this thesis is to set the groundwork for future research on developing a machine-learning based anomaly detection system for hospitalized patients. Our first step was to study and analyze the project’s needs, background, and literature examining similar criteria. In the second step, we interviewed medical experts and researchers. Based on our research and the suggestions received in our interviews, we explored methods that could be utilized to approach the issue based on the data we collected. The results of these approaches were then discussed. According to the results, the K-means algorithm, which utilizes principle components to cluster, obtained the highest quality. We then discussed how other algorithms have been influenced more by the shape of the data than by the values of the data. Afterward, we made some suggestions about how this research could be approached in the future as we move forward.en_US
dc.identifier.urihttps://hdl.handle.net/10037/31671
dc.language.isoengen_US
dc.publisherUiT Norges arktiske universitetno
dc.publisherUiT The Arctic University of Norwayen
dc.rights.holderCopyright 2023 The Author(s)
dc.rights.urihttps://creativecommons.org/licenses/by-nc-sa/4.0en_US
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)en_US
dc.subject.courseIDDTE-3900
dc.subjectClusteringen_US
dc.subjectUnsupervised Learningen_US
dc.subjectMachine Learningen_US
dc.subjectK-meansen_US
dc.subjectHierarchical Clusteringen_US
dc.titleClustering of clinical multivariate time-series utilizing recent advances in machine-learningen_US
dc.typeMaster thesisen
dc.typeMastergradsoppgaveno


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Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)