Machine learning improves early prediction of small-for-gestational-age births and reveals nuchal fold thickness as unexpected predictor.

Abstract

Authors

• Shier Nee Saw
  Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), Singapore.
• Arijit Biswas
  Department of Obstetrics and Gynecology, Yong Loo Lin School of Medicine, National University of Singapore, National University Health Systems, Singapore.
• Citra Nurfarah Zaini Mattar
  Department of Obstetrics and Gynecology, Yong Loo Lin School of Medicine, National University of Singapore, National University Health Systems, Singapore.
• Hwee Kuan Lee
  Daniel Aitor Holdbrook, Malay Singh, Emarene Mationg Kalaw, and Hwee Kuan Lee, Bioinformatics Institute; Malay Singh and Hwee Kuan Lee, National University of Singapore; Yukti Choudhury and Min-Han Tan, Lucence Diagnostics; Yukti Choudhury and Min-Han Tan, Institute of Bioengineering and Nanotechnology; Valerie Koh, Puay Hoon Tan, and John Yuen Shyi Peng, Singapore General Hospital; Hui Shan Tan, Ravindran Kanesvaran, and Min-Han Tan, National Cancer Center Singapore; and Hwee Kuan Lee, Institute for Infocomm Research, Singapore.
• Choon Hwai Yap
  Department of Bioengineering, Imperial College London, London, UK.

Keywords

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