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Table 2 Prediction accuracy of the superlearner estimating the risk of diabetic kidney disease in patients with type 2 diabetes mellitus and normal renal function

From: A SuperLearner approach for predicting diabetic kidney disease upon the initial diagnosis of T2DM in hospital

 

Value (95% CI)

Variable

Training cohort

(N = 2303)

Validation cohort

(N = 988)

#DKD

394 (17.11%)

169 (17.11%)

AUC

0.9378 (0.9272, 0.9484)

0.7138 (0.673, 0.7546)

Cutoff probability

0.15

0.15

Sensitivity, %

95.94 (93.49, 97.66)

73.37 (66.04, 79.87)

Specificity, %

67.73 (65.58, 69.83)

59.10 (55.64, 62.49)

PPV, %

38.03 (35.00, 41.13)

27.02 (23.00, 31.33)

NPV, %

98.78 (98.02, 99.30)

91.49 (88.78, 93.73)

Positive likelihood ratio

2.9732 (2.7775, 3.1827)

1.7938 (1.5869, 2.0277)

Negative likelihood ratio

0.0600 (0.0371, 0.0970)

0.4506 (0.3486, 0.5824)

  1. *roc.test() for two receiver operator characteristic curves, P > 0.99
  2. Abbreviations: AUC, area under the curve; CI, confidence interval; NPV, negative predictive value; PPV, positive predictive value
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BMC Medical Informatics and Decision Making

ISSN: 1472-6947

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