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