Version 1
: Received: 25 March 2018 / Approved: 26 March 2018 / Online: 26 March 2018 (09:59:51 CEST)
How to cite:
Nguyen, L.; Ho, T.T. Early Fetal Weight Estimation with Expectation Maximization Algorithm. Preprints2018, 2018030212. https://doi.org/10.20944/preprints201803.0212.v1
Nguyen, L.; Ho, T.T. Early Fetal Weight Estimation with Expectation Maximization Algorithm. Preprints 2018, 2018030212. https://doi.org/10.20944/preprints201803.0212.v1
Nguyen, L.; Ho, T.T. Early Fetal Weight Estimation with Expectation Maximization Algorithm. Preprints2018, 2018030212. https://doi.org/10.20944/preprints201803.0212.v1
APA Style
Nguyen, L., & Ho, T.T. (2018). Early Fetal Weight Estimation with Expectation Maximization Algorithm. Preprints. https://doi.org/10.20944/preprints201803.0212.v1
Chicago/Turabian Style
Nguyen, L. and Thu-Hang T. Ho. 2018 "Early Fetal Weight Estimation with Expectation Maximization Algorithm" Preprints. https://doi.org/10.20944/preprints201803.0212.v1
Abstract
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 but the regression model requires that time points of collecting such measures must not be too far from last ultrasound scans. Therefore this research proposes a method of early weight estimation based on expectation maximization (EM) algorithm so that ultrasound measures can be taken at any time points in gestational period. In other words, gestational sample can lack some or many fetus weights, which gives facilities to practitioners because practitioners need not concern fetus weights when taking ultrasound examinations. The proposed method is called dual regression expectation maximization (DREM) algorithm. Experimental results indicate that accuracy of DREM decreases insignificantly when completion of ultrasound sample decreases significantly. So it is proved that DREM withstands missing values in incomplete sample or sparse sample.
Medicine and Pharmacology, Obstetrics and Gynaecology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
