Abstract
Accurate acquisition of expected fetal weight (EFW) based on ultrasound measurements is important to antenatal care. The accuracy of EFW is disturbed by random error of measurements and impropriety of regression method. There have been several studies using neural networks to improve estimation validity, but these methods are all on the premises of measurements accuracy. This paper utilizes the fuzzy logic to deal with the measurements inconsistence, while combines with the support vector regression (SVR) to pursue generalization ability. By this way, the suspect inaccurate measurements can have relatively less contributions to the learning of new fuzzy support vector regression (FSVR). Tests on a clinical database show that proposed algorithm can achieve 6.09% mean absolute percent error (MAPE) for testing group while the back-propagation algorithm and classical SVR achieve 8.95% and 7.23% MAPE respectively. Experimental results show the effectives of the proposed algorithm over traditional methods based on neural network.
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References
Dudley, N.J.: A Systematic Review of the Ultrasound Estimation of Fetal Weight. Ultrasound Obstet. Gynecol. 25, 80–89 (2004)
Farmer, R.M., Medearis, A.L., Hirata, G.I., Platt, L.D.: The Use of A Neural Network for the Ultrasound Estimation of Fetal Weight in the Macrosomic Fetus. AM J. Obstet. Gynecol. 166, 1467–1472 (1992)
Chuang, L., Hwang, J.Y., Chang, C.H., Yu, C.H., Chang, F.M.: Ultrasound Estimation of Fetal Weight with the Use of Computerized Artificial Neural Network Model. Ultrasound in Med. & Biol. 28, 991–996 (2002)
Vapnik, V.N.: Statistical learning theory. Wiley, New York (1998)
Song, X.F., Han, P., Zou, L., Chen, D.Z., Hu, S.X.: A New Method for Estimation of Fetal Weight Using Support Vector Machine. Chinese Journal of Biomedical Engineering 23, 516–522 (2004)
Zhang, X.: Using Class-center Vectors to Build Support Vector Machines. In: Proc. IEEE NNSP, pp. 3–11 (1999)
Lin, C.H., Wang, S.D.: Fuzzy Support Vector Machines. IEEE Trans. on Neural Networks 13, 464–471 (2002)
Mangasarian, O.L., David, R.: Successive Over-relaxation for Support Vector Machines. IEEE Trans. on Neural Networks 10, 1032–1037 (1999)
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Yu, JH., Wang, YY., Chen, P., Song, YH. (2007). Ultrasound Estimation of Fetal Weight with Fuzzy Support Vector Regression. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72395-0_54
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DOI: https://doi.org/10.1007/978-3-540-72395-0_54
Publisher Name: Springer, Berlin, Heidelberg
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