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Table 3 Discrimination results of predicting neonatal jaundice with CRF and GV under CN220 guideline

From: Ensemble learning for the early prediction of neonatal jaundice with genetic features

Variables

Method

AUC

F1-score

Precision

CRF

Lightgbm

0.792 (0.757–0.828)

0.213 (0.171–0.251)

0.136 (0.109–0.161)

 

Cart

0.553 (0.509–0.592)

0.150 (0.074–0.211)

0.137 (0.074–0.191)

 

Logistic

0.785 (0.753–0.821)

0.210 (0.178–0.240)

0.122 (0.103–0.141)

 

Naive Bayes

0.735 (0.673–0.782)

0.165 (0.129–0.188)

0.091 (0.069–0.104)

 

rf

0.766 (0.711–0.806)

0.206 (0.177–0.245)

0.123 (0.106–0.147)

GV36

Lightgbm

0.603 (0.546–0.662)

0.149 (0.105–0.189)

0.105 (0.074–0.131)

 

Cart

0.558 (0.522–0.598)

0.149 (0.105–0.191)

0.110 (0.079–0.139)

 

Logistic

0.569 (0.519–0.614)

0.118 (0.093–0.141)

0.068 (0.053–0.081)

 

Naive bays

0.562 (0.509–0.622)

0.112 (0.106–0.116)

0.059 (0.057–0.062)

 

rf

0.587 (0.522–0.652)

0.148 (0.104–0.197)

0.103 (0.074–0.136)

CRF_GV36

Lightgbm

0.820 (0.785–0.857)

0.277 (0.218–0.333)

0.204 (0.160–0.247)

 

Cart

0.569 (0.517–0.621)

0.184 (0.103–0.269)

0.175 (0.095–0.250)

 

Logistic

0.781 (0.730–0.816)

0.218 (0.185–0.251)

0.129 (0.110–0.150)

 

Naive Bayes

0.642 (0.563–0.707)

0.114 (0.105–0.124)

0.061 (0.056–0.067)

 

rf

0.792 (0.753–0.833)

0.228 (0.193–0.259)

0.139 (0.118–0.158)

  1. The best performance by algorithms with CRF, GV36 and CRF_GV36 variables are marked in bold
  2. 95% CI is shown in parentheses