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Table 2 Comparison of the application of different algorithms to data subsets in terms of accuracy and specificity (for sensitivity of 90%)

From: Applying data mining techniques to improve diagnosis in neonatal jaundice

Algorithms Subsets
  CRF TcB at 24 h TcB and CRF at 24 h
  AUC 95% CI SPE AUC 95% CI SPE AUC 95% CI SPE
J48 0.47 (0.42-0.52) 0.09 0.79 (0.74-0.84) 0.43 0.75 (0.70-0.80) 0.33
Simple Cart 0.46 (0.41-0.51) 0.10 0.76 (0.71-0.81) 0.42 0.77 (0.72-0.82) 0.41
Naive Bayes 0.72 (0.67-0.77) 0.38 0.82 (0.77-0.87) 0.54 0.88 (0.84-0.92) 0.56
Bayes Net 0.74 (0.69-0.79) 0.42 0.73 (0.68-0.78) 0.35 0.87 (0.83-0.91) 0.60
MP 0.70 (0.65-0.75) 0.35 0.84 (0.80-0.88) 0.53 0.81 (0.76-0.86) 0.50
SMO 0.53 (0.48-0.58) 0.15 0.50 (0.45-0.55) 0.12 0.72 (0.67-0.77) 0.54
Simple Logistic 0.72 (0.67-0.77) 0.39 0.80 (0.75-0.85) 0.41 0.89 (0.85-0.93) 0.56
  1. MP – Multilayer Perceptron; SMO – Sequential Minimal Optimization; AUC – Area under the receiving-operator characteristic curve; CI – Confidence interval; SPE – Specificity; CRF – Clinical Risk Factors; TcB – Transcutaneous bilirubin.