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The study explored how to effectively integrate high-dimensional genetic features into predicting neonatal jaundice.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Our study shed light on the theoretical and practical value of GV in the prediction of neonatal jaundice.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12911-021-01701-9","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T20:02:37Z","timestamp":1638388957000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Ensemble learning for the early prediction of neonatal jaundice with genetic features"],"prefix":"10.1186","volume":"21","author":[{"given":"Haowen","family":"Deng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Youyou","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cheng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,12,1]]},"reference":[{"key":"1701_CR1","doi-asserted-by":"publisher","first-page":"c23409","DOI":"10.1136\/bmj.c2409","volume":"340","author":"J Rennie","year":"2010","unstructured":"Rennie J, Burman-Roy S, Murphy S. 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