Using neural networks to predict the outcome of refractive surgery for myopia

Open Access

Table 5

Measuring sensitivity, speciﬁcity, and Cohen’s Kappa statistic (k-Cohen).

Nets	Positive (retreat case)		Negative (non-retreat case)
Net 1	True: 52		True: 1651
	False: 18		False: 657
	Total: 70		Total: 2308
	Sensitivity: 52/70 = 0.7466		Speciﬁcity: 1651/2308 = 0.7154
		k-Cohen: 0.7158
Net 2	True: 50		True: 1509
	False: 20		False: 799
	Total: 70		Total: 2308
	Sensitivity: 50/70 = 0.7143		Speciﬁcity: 1509/2308 = 0.6538
		k-Cohen: 0.6552
Net 3	True: 46		True: 1598
	False: 24		False: 710
	Total: 70		Total: 2308
	Sensitivity: 46/70 = 0.6571		Speciﬁcity: 1598/2308 = 0.6924
		k-Cohen: 0.6910
Net 4	True: 49		True: 1704
	False: 21		False: 604
	Total: 70		Total: 2308
	Sensitivity: 49/70 = 0.7000		Speciﬁcity: 1704/2308 = 0.7385
		k-Cohen: 0.7369
Net 5	True: 46		True: 1616
	False: 24		False: 692
	Total: 70		Total: 2308
	Sensitivity: 46/70 = 0.6571		Speciﬁcity: 1616/2308 = 0.7002
		k-Cohen: 0.6985
Net 6	True: 48		True: 1620
	False: 22		False: 688
	Total: 70		Total: 2308
	Sensitivity: 48/70 = 0.6857		Speciﬁcity: 1620/2308 = 0.7019
		k-Cohen: 0.7011
Net 7	True: 45		True: 1669
	False: 25		False: 639
	Total: 70		Total: 2308
	Sensitivity: 45/70 = 0.6429		Speciﬁcity: 1669/2308 = 0.7231
		k-Cohen: 0.7204
Net 8	True: 49		True: 1598
	False: 21		False: 710
	Total: 70		Total: 2308
	Sensitivity: 49/70 = 0.7000		Speciﬁcity: 1598/2308 = 0.7018
		k-Cohen: 0.6922

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