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Predicting Meibomian Gland Dropout and Feature Importance Analysis with Explainable Artificial Intelligence

Permanent lenke
https://hdl.handle.net/10037/32991
DOI
https://doi.org/10.1109/CBMS58004.2023.00245
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Åpne
article.pdf (1.168Mb)
Akseptert manusversjon (PDF)
Dato
2023-07-17
Type
Journal article
Tidsskriftartikkel
Peer reviewed

Forfatter
Fineide, Fredrik; Storås, Andrea; Riegler, Michael Alexander; Utheim, Tor Paaske
Sammendrag
Dry 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.
Forlag
IEEE
Sitering
Fineide, Storås, Riegler, Utheim. Predicting Meibomian Gland Dropout and Feature Importance Analysis with Explainable Artificial Intelligence. IEEE International Symposium on Computer-Based Medical Systems. 2023
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  • Artikler, rapporter og annet (informatikk) [481]
Copyright 2023 The Author(s)

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