A First Approach to Birth Weight Prediction Using RBFNNs

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.