Evaluating the Performance of Various Machine Learning Algorithms to Detect Subclinical Keratoconus
Ke Cao, Karin Verspoor, Srujana Sahebjada, Paul N Baird
Translational Vision Science and Technology | Association for Research in Vision and Ophthalmology | Published : 2020
Purpose: Keratoconus (KC) represents one of the leading causes of corneal transplantation worldwide. Detecting subclinical KC would lead to better management to avoid the need for corneal grafts, but the condition is clinically challenging to diagnose. We wished to compare eight commonly used machine learning algorithms using a range of parameter combinations by applying them to our KC dataset and build models to better differentiate subclinical KC from non-KC eyes. Methods: Oculus Pentacam was used to obtain corneal parameters on 49 subclinical KC and 39 control eyes, along with clinical and demographic parameters. Eight machine learning methods were applied to build models to differentiat..View full abstract
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Awarded by National Health and Medical Research Council (NHMRC)
This study was supported by the Australian National Health and Medical Research Council (NHMRC) project Ideas grant APP1187763 and Senior Research Fellowship (1138585 to PNB), the Louisa Jean De Bretteville Bequest Trust Account, University of Melbourne, the Angior Family Foundation, Keratoconus Australia, Perpetual Impact Philanthropy grant (SS) and a Lions Eye Foundation Fellowship (SS). The Centre for Eye Research Australia (CERA) receives Operational Infrastructure Support from the Victorian Government.