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- Title
Ensemble learning for the early prediction of neonatal jaundice with genetic features.
- Authors
Deng, Haowen; Zhou, Youyou; Wang, Lin; Zhang, Cheng
- Abstract
<bold>Background: </bold>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.<bold>Methods: </bold>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.<bold>Results: </bold>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.<bold>Conclusions: </bold>Our study shed light on the theoretical and practical value of GV in the prediction of neonatal jaundice.
- Subjects
CHINA; NEONATAL jaundice; GENETIC variation; NEWBORN infants; HOSPITALS; DIAGNOSIS; FORECASTING
- Publication
BMC Medical Informatics & Decision Making, 2021, Vol 21, Issue 1, p1
- ISSN
1472-6947
- Publication type
journal article
- DOI
10.1186/s12911-021-01701-9