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eSymposia | Optimizing Nutrition for Maternal, Newborn and Child Health


Early Prediction of Small-for-Gestational-Age (SGA) Babies at Second Trimester using Machine Learning Models


Oct 21, 2020 12:00am ‐ Oct 21, 2020 12:00am

Description

Early Prediction of Small-for-Gestational-Age (SGA) Babies at Second Trimester using Machine Learning Models Objective: To investigate the performance of the machine learning (ML) model in predicting small-for-gestational-age (SGA) at birth in the second trimester. Methods: Retrospective data of 347 patients, consisting of maternal demographics and ultrasound parameters collected between the 20th and 25th gestational weeks, were studied. ML models were applied to different combinations of the parameters to predict SGA and severe 49 SGA at birth (defined as 10th and 3rd centile birth weight). Results: ML models achieved an sensitivity of 62-69% in predicting SGA whereas clinical evaluation via guidelines had an sensitivity of 34% in predicting SGA. ML models showed that nuchal fold thickness (NF) was an important parameter in the prediction. Statistical analysis showed that SGA newborns had significantly reduced NF in the second trimester. Conclusion: ML can improve the prediction of SGA at birth from second-trimester measurements, and demonstrated that reduced NF to be an important predictor. Early prediction of SGA allows closer clinical monitoring, which provides an opportunity to discover any underlying diseases associated with SGA. We acknowledge that this research doesn’t directly relate to nutrition, but rather, it helps identify mothers and their babies that would benefit from nutritional intervention earlier than is possible using current methods. Shier Nee Saw1, Citra Nurfarah Zaini Mattar2, ArijitBiwas2, Hwee Kuan Lee3,4,5, Choon Hwai Yap1 1. Department of Biomedical Engineering, National University of Singapore, Singapore; 2. Department of Obstetrics and Gynecology, Yong Loo Lin School of Medicine, National University of Singapore, National University Health Systems, Singapore; 3. Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR). 4. CNRS, IPAL, Singapore, Singapore. 5. Department of Computer Science, School of Computing, National University of Singapore, Singapore

Speaker(s):

  • Shier Nee Saw, PhD, Bioinformatics Institute, Agency for Science, Technology and Research

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