Experimental Medicine, Vol. 1, Issue 2, Dec  2018, Pages 45-65; DOI: 10.31058/j.em.2018.12004 10.31058/j.em.2018.12004

Fetal Weight Estimation in Case of Missing Data

Experimental Medicine, Vol. 1, Issue 2, Dec  2018, Pages 45-65.

DOI: 10.31058/j.em.2018.12004

Loc Nguyen 1* , Thu-Hang T. Ho 2

1 Independent Scholar, Loc Nguyen’s Academic Network, An Giang, Vietnam

2 Board of Directors, Vinh Long General Hospital, Vinh Long, Vietnam

Received: 5 July 2018; Accepted: 30 September 2018; Published: 17 December 2018

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Fetal weight estimation before delivery is important in obstetrics, which assists doctors diagnose abnormal or diseased cases. Linear regression based on ultrasound measures such as bi-parietal diameter (bpd), head circumference (hc), abdominal circumference (ac), and fetal length (fl) is common statistical method for weight estimation. There is a demand to retrieve regression model in case of incomplete data because taking ultrasound examinations is a hard task and early weight estimation is necessary in some cases. In this research, we proposed so-called regression expectation maximization (REM) algorithm which is a combination of linear regression method and expectation maximization (EM) method to construct the regression model when both ultrasound measures and fetal weight are missing. The special technique in REM is to build parallelly an entire regression function and many partial inverse regression functions for solving the problem of highly sparse data, in which missing values are fulfilled by expectations relevant to both entire regression function and inverse regression functions. Experimental results proved resistance of REM to incomplete data, in which accuracy of REM decreases insignificantly when data sample is made sparse with loss ratios up to 80%.


Fetal Weight Estimation, Regression Model, Ultrasound Measures, Expectation Maximization Algorithm, Missing Data


© 2017 by the authors. Licensee International Technology and Science Press Limited. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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