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Rule Extraction for Glaucoma Detection with Summary Data from StratusOCT

¹From the Department of Industrial Engineering and Management, National Chin-Yi Institute of Technology, Taipei, Taichung, Taiwan; and the ²Glaucoma Service, Department of Ophthalmology, China Medical University Hospital Taichung City, Taiwan.

PURPOSE. To extract and induce rules of association for differentiating between normal and glaucomatous eyes based on the quantitative assessment of summary data reports from the StratusOCT (optical coherence tomography; Carl Zeiss Meditec, Inc., Dublin, CA) in a Taiwan Chinese population.

METHODS. One randomly selected eye of each of the 64 patients with glaucoma and each of the 71 normal subjects was included in the study. Measurements of glaucoma variables (retinal nerve fiber layer thickness and optic nerve head analysis results) were obtained with the StratusOCT. A self-organizing map and decision tree were applied to extract features and determine rules of association for glaucoma detection.

RESULTS. The average visual field mean deviation was –0.55 ± 0.57 dB in the normal group and –4.30 ± 3.32 dB in the glaucoma group. Vertical cup-to-disc (C/D) ratio and inferior quadrant thickness were extracted from the decision tree, and three association rules were determined for glaucoma detection.

CONCLUSIONS. The precise rules of association induced by a novel application of the decision tree may enhance glaucoma detection.

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