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Advances in Corneal Diagnostics Using Machine Learning

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dc.citedby0
dc.contributor.authorAl-Sharify N.T.en_US
dc.contributor.authorYussof S.en_US
dc.contributor.authorGhaeb N.H.en_US
dc.contributor.authorAl-Sharify Z.T.en_US
dc.contributor.authorNaser H.Y.en_US
dc.contributor.authorAhmed S.M.en_US
dc.contributor.authorSee O.H.en_US
dc.contributor.authorWeng L.Y.en_US
dc.contributor.authorid57205364615en_US
dc.contributor.authorid16023225600en_US
dc.contributor.authorid26428056100en_US
dc.contributor.authorid57204908487en_US
dc.contributor.authorid57218554005en_US
dc.contributor.authorid57696704100en_US
dc.contributor.authorid16023044400en_US
dc.contributor.authorid59489098700en_US
dc.date.accessioned2025-03-03T07:41:22Z
dc.date.available2025-03-03T07:41:22Z
dc.date.issued2024
dc.description.abstractThis paper provides comprehensive insights into the cornea and its diseases, with a particular focus on keratoconus. This paper explores the cornea?s function in maintaining ocular health, detailing its anatomy, pathological conditions, and the latest developments in diagnostic techniques. Keratoconus is discussed extensively, covering its subtypes, etiology, clinical manifestations, and the application of the Q-value for quantification. Several diagnostic techniques, such as corneal topography, are crucial points of discussion. This paper also examines the use of machine learning models, specifically Decision Tree and Nearest Neighbor Analysis, which enhance the accuracy of diagnosing based on topographical corneal parameters from corneal topography. These models provide valuable insights into disease progression and aid in clinical decision making. Integrating these technologies in medical research opens promising avenues for enhanced disease detection. Our findings demonstrate the effectiveness of Decision Tree and Nearest Neighbor Analysis in classifying and predicting conditions based on corneal parameters. The Decision Tree achieved classification accuracy of 62% for training and 65.7% for testing, while Nearest Neighbor Analysis yielded 65.4% for training and 62.6% for holdout samples. These models offer valuable insights into the progression and severity of keratoconus, aiding clinicians in treatment and management decisions. ? 2024 by the authors.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo1198
dc.identifier.doi10.3390/bioengineering11121198
dc.identifier.issue12
dc.identifier.scopus2-s2.0-85213222534
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85213222534&doi=10.3390%2fbioengineering11121198&partnerID=40&md5=80f09a909aaeca543c879c43829c61ee
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36090
dc.identifier.volume11
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)en_US
dc.relation.ispartofAll Open Access; Gold Open Access
dc.sourceScopus
dc.sourcetitleBioengineering
dc.titleAdvances in Corneal Diagnostics Using Machine Learningen_US
dc.typeArticleen_US
dspace.entity.typePublication
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