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dc.contributor.authorFineide, Fredrik
dc.contributor.authorStorås, Andrea
dc.contributor.authorRiegler, Michael Alexander
dc.contributor.authorUtheim, Tor Paaske
dc.date.accessioned2024-02-20T13:37:21Z
dc.date.available2024-02-20T13:37:21Z
dc.date.issued2023-07-17
dc.description.abstractDry eye disease is a common and potentially debilitating medical condition. Meibum secreted from the meibomian glands is the largest contributor to the outermost, protective lipid layer of the tear film. Dysfunction of the meibomian glands is the most common cause of dry eye disease. As meibomian gland dysfunction progresses, gradual atrophy of the glands is observed. The meibomian glands are commonly visualized through meibography, a technique requiring specialist equipment and knowledge that might not be available to the physician. In the present project we use machine learning on clinical tabular data to predict the degree of meibomian gland dropout. Moreover, we employ explainable artificial intelligence on the best performing algorithms for feature importance evaluation. The best performing algorithms were AdaBoost, multilayer perceptron and LightGBM which outperformed the majority vote baseline classifier in every included evaluation metric for both multioutput and binary classification. Through explainable artificial intelligence known associations are validated and novel connections identified and discussed.en_US
dc.identifier.citationFineide, Storås, Riegler, Utheim. Predicting Meibomian Gland Dropout and Feature Importance Analysis with Explainable Artificial Intelligence. IEEE International Symposium on Computer-Based Medical Systems. 2023en_US
dc.identifier.cristinIDFRIDAID 2212325
dc.identifier.doi10.1109/CBMS58004.2023.00245
dc.identifier.issn2372-9198
dc.identifier.urihttps://hdl.handle.net/10037/32991
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.relation.journalIEEE International Symposium on Computer-Based Medical Systems
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2023 The Author(s)en_US
dc.titlePredicting Meibomian Gland Dropout and Feature Importance Analysis with Explainable Artificial Intelligenceen_US
dc.type.versionacceptedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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