Abstract
This paper presents a first approach to try to determine the weight of a newborn using a set of variables determined uniquely by the mother. The proposed model to approximate the weight is a Radial Basis Function Neural Network (RBFNN) because it has been successfully applied to many real world problems. The problem of determining the weight of a newborn could be very useful by the time of diagnosing the gestational diabetes mellitus, since it can be a risk factor, and also to determine if the newborn is macrosomic. However, the design of RBFNNs is another issue which still remains as a challenge since there is no perfect methodology to design an RBFNN using a reduced data set, keeping the generalization capabilities of the network. Within the many design techniques existing in the literature, the use of clustering algorithms as a first initialization step for the RBF centers is a quite common solution and many approaches have been proposed. The following work presents a comparative of RBFNNs generated using several algorithms recently developed concluding that, although RBFNNs that can approximate a training data set with an acceptable error, further work must be done in order to adapt RBFNN to large dimensional spaces where the generalization capabilities might be lost.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum, New York (1981)
Bors, A.G.: Introduction of the Radial Basis Function (RBF) networks. In: OnLine Symposium for Electronics Engineers, vol. 1, pp. 1–7 (2001)
Dyck, R., Klomp, H., Tan, L.K., Turner, R.W., Boctor, M.A.: A comparison of rates, risk factors, and outcomes of gestational diabetes between aboriginal and non-aboriginal women in the Saskatoon Health District. Diabetes Care 25, 487–493 (2002)
Gersho, A.: Asymptotically Optimal Block Quantization. IEEE Transanctions on Information Theory 25(4), 373–380 (1979)
González, J., Rojas, I., Ortega, J., Pomares, H., Fernández, F.J., Díaz, A.: Multiobjective evolutionary optimization of the size, shape, and position parameters of radial basis function networks for function approximation. IEEE Transactions on Neural Networks 14(6), 1478–1495 (2003)
Guillén, A., Rojas, I., González, J., Pomares, H., Herrera, L.J., Prieto, A.: A Fuzzy-Possibilistic Fuzzy Ruled Clustering Algorithm for RBFNNs Design. In: Greco, S., Hata, Y., Hirano, S., Inuiguchi, M., Miyamoto, S., Nguyen, H.S., Słowiński, R. (eds.) RSCTC 2006. LNCS (LNAI), vol. 4259, pp. 647–656. Springer, Heidelberg (2006)
Guillén, A., Rojas, I., González, J., Pomares, H., Herrera, L.J., Prieto, A.: Supervised RBFNN Centers and Radii Initialization for Function Approximation Problems. In: International Joint Conference on Neural Networks, IJCNN ’06, July 2006, pp. 5814–5819 (2006)
Guillén, A., Rojas, I., González, J., Pomares, H., Herrera, L.J., Valenzuela, O., Prieto, A.: A Possibilistic Approach to RBFN Centers Initialization. In: Ślęzak, D., Yao, J., Peters, J.F., Ziarko, W., Hu, X. (eds.) RSFDGrC 2005. LNCS (LNAI), vol. 3642, pp. 174–183. Springer, Heidelberg (2005)
Guillén, A., Rojas, I., González, J., Pomares, H., Herrera, L.J., Valenzuela, O., Prieto, A.G.: Improving Clustering Technique for Functional Approximation Problem Using Fuzzy Logic: ICFA Algorithm. In: Cabestany, J., Prieto, A.G., Sandoval, F. (eds.) IWANN 2005. LNCS, vol. 3512, pp. 272–279. Springer, Heidelberg (2005)
Karayiannis, N.B., Mi, G.W.: Growing radial basis neural networks: Merging supervised and unsupervised learning with network growth techniques. IEEE Transactions on Neural Networks 8, 1492–1506 (1997)
Moody, J., Darken, C.J.: Fast learning in networks of locally-tunned processing units. Neural Computation 1(2), 281–294 (1989)
Park, J., Sandberg, J.W.: Universal approximation using radial basis functions network. Neural Computation 3, 246–257 (1991)
Patanè, G., Russo, M.: The Enhanced-LBG algorithm. Neural Networks 14(9), 1219–1237 (2001)
Rojas, I., Anguita, M., Prieto, A., Valenzuela, O.: Analysis of the operators involved in the definition of the implication functions and in the fuzzy inference proccess. International Journal of Approximate Reasoning 19, 367–389 (1998)
Zamorski, M.A., Biggs, W.S.: Management of Suspected Fetal Macrosomia. American Family Physician 63(2) (2001)
Zhang, J., Leung, Y.: Improved possibilistic C–means clustering algorithms. IEEE Transactions on Fuzzy Systems 12, 209–217 (2004)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Guillén, A., Rojas, I., González, J., Pomares, H., Herrera, L.J. (2007). A First Approach to Birth Weight Prediction Using RBFNNs. In: Mira, J., Álvarez, J.R. (eds) Bio-inspired Modeling of Cognitive Tasks. IWINAC 2007. Lecture Notes in Computer Science, vol 4527. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73053-8_25
Download citation
DOI: https://doi.org/10.1007/978-3-540-73053-8_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73052-1
Online ISBN: 978-3-540-73053-8
eBook Packages: Computer ScienceComputer Science (R0)