Table 2.

AUC Values from Analyses of Different Binary Classifiers

Classifier	AUC	AUC	Specificity	Specificity
	All Diabetic vs. Normal	DR vs. DnR	All Diabetic vs. Normal	DR vs. DnR
Naïve Bayes	0.78 (0.70, 0.86)	0.90 (0.83, 0.95)	0.43 (0.29, 0.59)	0.68 (0.40, 0.88)
Decision Tree	0.61 (0.52, 0.70)	0.83 (0.75, 0.91)	0.12 (0.04, 0.24)	0.46 (0.23, 0.78)
Logistic Regression	0.79 (0.71, 0.87)	0.91 (0.85, 0.96)	0.47 (0.31, 0.63)	0.72 (0.56, 0.88)
Random Forest	0.80 (0.73, 0.87)	0.86 (0.79, 0.93)	0.49 (0.31, 0.69)	0.66 (0.34, 0.82)
Gradient Boosting	0.75 (0.67, 0.83)	0.84 (0.76, 0.92)	0.41 (0.12, 0.59)	0.62 (0.22, 0.80)

Values for AUC for each of the various forms of machine learning and statistical analysis that have been applied are given with respect to the classification tasks in the first two columns; N versus DM individuals and DR versus DnR. A perfect classifier has an AUC value of 1. In the rightmost two columns, specificity estimates of each of the binary classifiers are provided for a fixed 90% value of sensitivity. Values for the 95% confidence intervals are shown in brackets.