TABLE 2.

Comparison of machine learning approaches for global visual field mean deviation modeling

	Asian internal test set			Caucasian external test set
Approach	Pearson's correlation	Mean absolute error (MAE) a	Baseline MAE change (%) b	Pearson's correlation	Mean absolute error (MAE) a	Baseline MAE change (%) b
Baseline	–	3.80		–	4.09	–
Linear regression (LR)	0.43 [0.34, 0.57]	3.47 [2.97, 3.94]	−8.7 [−21.8, +3.7]	0.39 [0.24,0.50]	3.86 [3.54, 5.11]	−5.6 [−13.4, +24.9]
Elastic net regression (ENR)	0.51 [0.37, 0.59]	3.19 [2.74, 3.71]	−16.1 [−27.9, −2.7]	0.51 [0.36, 0.67]	3.45 [2.95, 4.51]	−15.6 [−27.9, +10.3]
Support vector regression (SVR)	0.54 [0.39, 0.64]	3.09 [2.58, 3.67]	−18.7 [−32.1, −3.4]	0.59 [0.35, 0.77]	3.15 [2.73, 4.39]	−23.0 [−33.3, +7.3]
Multi‐layer perceptron (MLP)	0.42 [0.21, 0.55]	3.35 [2.84, 3.96]	−11.4 [−25.3, +4.2]	0.61 [0.35, 0.76]	3.16 [2.87, 4.34]	−22.7 [−29.8, +6.1]
Convolutional neural net (CNN)	0.51 [0.35, 0.61]	3.25 [2.81, 3.79]	−14.5 [−26.1, −0.3]	0.62 [0.39, 0.79]	3.13 [2.70, 4.18]	−23.4 [−34.0, +2.2]
Gradient‐boosted trees (XGB)	0.59 [0.47, 0.66]	3.01 [2.57, 3.48]	−20.7 [−32.4, −8.4]	0.66 [0.46, 0.83]	3.04 [2.59, 3.99]	−25.5 [−36.7, −2.4]

Note: Values presented as means with 95% confidence intervals adjusted for intereye correlations using bootstrapping clustered by subject. Bolded values are significantly lower (p < 0.05) than baseline MAE within the corresponding test set using the Wilcoxon signed‐rank test.

^a MAE values presented in dB.

^b Represents % reduction of MAE from baseline MAE, where negative (−) values represent a reduction in MAE and positive (+) values represent an increase in MAE with respect to baseline values.