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. 2021 Dec 1;21(1):338.
doi: 10.1186/s12911-021-01701-9.

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

Haowen Deng #  1 , Youyou Zhou #  2 , Lin Wang  3 , Cheng Zhang  4
Affiliations

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

Haowen Deng et al. BMC Med Inform Decis Mak. .

Abstract

Background: Neonatal jaundice may cause severe neurological damage if poorly evaluated and diagnosed when high bilirubin occurs. The study explored how to effectively integrate high-dimensional genetic features into predicting neonatal jaundice.

Methods: This study recruited 984 neonates from the Suzhou Municipal Central Hospital in China, and applied an ensemble learning approach to enhance the prediction of high-dimensional genetic features and clinical risk factors (CRF) for physiological neonatal jaundice of full-term newborns within 1-week after birth. Further, sigmoid recalibration was applied for validating the reliability of our methods.

Results: The maximum accuracy of prediction reached 79.5% Area Under Curve (AUC) by CRF and could be marginally improved by 3.5% by including genetic variant (GV). Feature importance illustrated that 36 GVs contributed 55.5% in predicting neonatal jaundice in terms of gain from splits. Further analysis revealed that the main contribution of GV was to reduce the false-positive rate, i.e., to increase the specificity in the prediction.

Conclusions: Our study shed light on the theoretical and practical value of GV in the prediction of neonatal jaundice.

Keywords: Genetic variants; Hyperbilirubinemia; Machine learning; Transcutaneous bilirubin.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
The methodological workflow of our study
Fig. 2
Fig. 2
The architecture of Gradient Boosting Decision Tree
Fig. 3
Fig. 3
Relative feature importance from ensemble nethod in predicting neonatal jaundice under CN220 guideline
Fig. 4
Fig. 4
Calibration curves on external validation sets
Fig. 5
Fig. 5
ROC curve of neonatal jaundice prediction with CRF and GV by ensemble learning
Fig. 6
Fig. 6
Comparison of ROC curve of neonatal jaundice prediction after introducing genetic variants (GV36)
See this image and copyright information in PMC

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