Case 161, color-fundus and red-free fundus photography (A), peripapillary RNFL thickness measured by SD-OCT (B), and automated 30–2 visual field test (C).

<p>Fundus photographs show an increased cup-to-disc ratio in both eyes and a RNFL defect in the left eye. SD-OCT shows a decrease in the peripapillary thickness of the infratemporal quadrant of the left eye. The visual field test demonstrates field defect in the left eye.</p

Case 81, color-fundus and red-free fundus photography (A), peripapillary RNFL thickness measured by SD-OCT (B), and automated 30–2 visual field test (C).

<p>Fundus photographs show an increased cup-to-disc ratio and RNFL defects in the both eyes. SD-OCT shows decrease in peripapillary thickness of inferotemporal quadrant for both eyes. Visual field defects are apparent in both eyes.</p

Final features list for building the training model.

<p>We removed the features that contained many missing values. We then performed <i>t</i>-tests against the rest of the features to see class separability of the features. The feature RNFL4.mean reflects mean of SUP-INF-TMP combination.</p

List of basic features from the examination data.

<p>We extracted them from examination records for glaucoma and healthy controls.</p

Demographic and clinical data of cases with differences between clinical diagnosis and algorithmic judgment.

<p>Demographic and clinical data of cases with differences between clinical diagnosis and algorithmic judgment.</p

Decision tree for diagnosis of glaucoma from C5.0 algorithm.

<p>It contains 19 rules and the training error of the model is 0.016.</p

PCA plot for prepared dataset.

<p>Each point means a case in the dataset. Generally, the glaucoma cases are well separated from the healthy control cases. Some cases are located in the border area or opposite area. Right plot shows relationship between distribution of cases and features. In the glaucoma group, PSD, GHT, ocular_presure, and age have high values whereas MD and RNFL4_mean have low values.</p

Characteristics of the participants.

<p>Characteristics of the participants.</p

ROC curve and AUC for four models.

<p>AUC expresses global quality of prediction models and RF and C5.0 models show 0.979, SVM is over 0.967, and KNN is 0.971. All models show very high values near 1.0.</p

Statistics of four learning models from classification tests.

<p>The RF model shows the best values on all evaluation criteria. Other models show similar performance.</p