Research Article

Comparison of Machine-Learning Classification Models for Glaucoma Management

Table 1

Extracted ocular parameters.

No.Quantification dataFeatures

1Patient’s background dataGender
2Age
3Spherical equivalent
4Mean deviation
5Pattern standard deviation
6Internal ocular pressure
7Central corneal thickness

8Optic disc shape parameters obtained from OCTDisc area
9Cup area
10Rim area
11Vertical disc diameter
12Horizontal disc diameter
13Vertical cup/disc diameter ratio
14Horizontal cup/disc diameter ratio
15Cup/disc area ratio
16Rim/disc area ratio
17Maximum cup depth
18Average cup depth
19–24Average rim/disc area ratio (six sectors)
25Rim decentering area ratio
26Horizontal disc angle
27Disc height difference
28Retinal pigment epithelium (RPE) height difference
29Disc tilt angle

30cpRNFLT average thickness obtained from OCTAverage cpRNFLT
31–34cpRNFLT (quadrants)
35Difference in cpRNFLT (superior and inferior in four sectors)
36–41cpRNFLT (six sectors)
42Rim decentering cpRNFLT ratio
43Difference in cpRNFLT (temporal superior and temporal inferior in six sectors)
44–55cpRNFLT (clockwise sectors)

56Ocular blood flow parameters obtained from LSFGAverage in all (tissue)
57–60Average in quadrants (tissue)
61Skewness in all (tissue)
62–65Skewness in quadrants (tissue)
66Blowout score in all (tissue)
67–70Blowout score in quadrants (tissue)
71Blowout time in all (tissue)
72–75Blowout time in quadrants (tissue)
76Rising rate in all (tissue)
77–80Rising rate in quadrants (tissue)
81Flow acceleration index in all (tissue)
82–85Flow acceleration index in quadrants (tissue)
86Acceleration time index in all (tissue)
87–90Acceleration time index in quadrants (tissue)
91Average ratio of blood stream