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 |