Evaluating machine learning classifiers for glaucoma referral decision support in primary care settings.

ABSTRACT: Several artificial intelligence algorithms have been proposed to help diagnose glaucoma by analyzing the functional and/or structural changes in the eye. These algorithms require carefully curated datasets with access to ocular images. In the current study, we have modeled and evaluated classifiers to predict self-reported glaucoma using a single, easily obtained ocular feature (intraocular pressure (IOP)) and non-ocular features (age, gender, race, body mass index, systolic and diastolic blood pressure, and comorbidities). The classifiers were trained on publicly available data of 3015 subjects without a glaucoma diagnosis at the time of enrollment. 337 subjects subsequently self-reported a glaucoma diagnosis in a span of 1-12 years after enrollment. The classifiers were evaluated on the

SUBMITTER: Kaskar OG 

PROVIDER: S-EPMC9122936 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature