Saw, Shier Nee and Biswas, Arijit and Mattar, Citra Nurfarah Zaini and Lee, Hwee Kuan and Yap, Choon Hwai (2021) Machine learning improves early prediction of small-for-gestational-age births and reveals nuchal fold thickness as unexpected predictor. Prenatal Diagnosis, 41 (4). pp. 505-516. ISSN 0197-3851, DOI https://doi.org/10.1002/pd.5903.
Full text not available from this repository.Abstract
Objective To investigate the performance of the machine learning (ML) model in predicting small-for-gestational-age (SGA) at birth, using second-trimester data. 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 SGA at birth (defined as 10th and third centile birth weight). Results Using second-trimester measurements, ML models achieved an accuracy of 70% and 73% in predicting SGA and severe SGA whereas clinical guidelines had accuracies of 64% and 48%. Uterine PI (Ut PI) was found to be an important predictor, corroborating with existing literature, but surprisingly, so was nuchal fold thickness (NF). Logistic regression showed that Ut PI and NF were significant predictors and statistical comparisons showed that these parameters were significantly different in disease. Further, including NF was found to improve ML model performance, and vice versa. Conclusion ML could potentially improve the prediction of SGA at birth from second-trimester measurements, and demonstrated 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.
| Item Type: | Article |
|---|---|
| Funders: | Agency for Science Technology & Research (ASTAR), Singapore Ministry of Education Academic Research Fund Tier 1 Grant 2016 |
| Uncontrolled Keywords: | Machine learning; Small-for-gestational-age births; small-for-gestational-age births |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology > Department of Artificial Intelligence |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 06 Jul 2022 02:35 |
| Last Modified: | 06 Jul 2022 02:35 |
| URI: | http://eprints.um.edu.my/id/eprint/27997 |
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