. Author manuscript; available in PMC: 2021 Dec 4.

Published in final edited form as: IEEE J Biomed Health Inform. 2020 Dec 4;24(12):3421–3430. doi: 10.1109/JBHI.2020.3001019

TABLE III.

5-FOLD AVERAGE PERFORMANCE MEASURES FOR GLAUCOMA DETECTION USING FEATURE-BASED MACHINE LEARNING METHODS

	Accuracy	MCC	Recall	Precision	F1-score	AUC
Extra Trees	85.563	0.436	87.367	96.160	91.498	89.556
Gradient Boosting	90.318	0.395	95.660	93.692	94.586	90.380
Logistic Regression	82.908	0.475	83.223	97.464	89.660	91.499
Naive Bayes	81.583	0.448	82.370	96.806	88.776	89.689
Random Forest	88.128	0.469	91.026	95.588	93.176	89.098
SVM (Linear)	81.120	0.454	81.202	97.477	88.450	91.550
SVM (Poly)	88.008	0.447	91.166	95.244	93.117	91.204
SVM (RBF)	80.749	0.442	81.126	97.140	88.254	90.602

Proposed AG-OCT	91.073	0.557	95.119	94.730	94.882	93.769