Fetal weight estimation using the evolutionary fuzzy support vector regression for low-birth-weight fetuses

Authors:
Jinhua Yu;Yuanyuan Wang;Ping Chen
Affiliations:
University of Missouri, Columbia, MO and Department of Electronic Engineering, Fudan University, Shanghai, China;Department of Electronic Engineering, Fudan University, Shanghai, China;First Maternity and Infant Health Hospital, Tongji University, Shanghai, China
Venue:
IEEE Transactions on Information Technology in Biomedicine
Year:
2009

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Abstract

Accurate estimation of fetal weight before delivery is of great benefit to limit the potential complication associated with the low-birth-weight infants. Although the regression analysis has been used as a daily clinical means to estimate the fetal weight on the basis of ultrasound measurements, it still lacks enough accuracy for low-birth-weight fetuses. The ineffectiveness ismainly due to the large inter- or intraobserver variability inmeasurements and the inappropriateness of the regression analysis. A novel method based on the support vector regression (SVR) is proposed to improve the weight estimation accuracy for fetuses of less than 2500 g. Here, fuzzy logic is introduced into SVR (termed FSVR) to limit the contribution of inaccurate training data to themodel establishment, and thus, to enhance the robustness of FSVR to noisy data. To guarantee the generalization performance of the FSVR model, the nondominated sorting genetic algorithm (NSGA) is utilized to obtain the optimal parameters for the FSVR, which is referred to as the evolutionary fuzzy support vector regression (EFSVR) model. Compared with regression formulas, back-propagation neural network, and SVR, EFSVR achieves the lowestmean absolute percent error (6.6%) and the highest correlation coefficient (0.902) between the estimated fetal weight and the actual birth weight. The EFSVR model produces significant improvement (1.9%-4.2%) on the accuracy of fetal weight estimation over severalwidely used formulas. Experiments showthe potential of EFSVR in clinical prenatal care.