title: Application of neural networks to the ranking of perinatal variables influencing birthweight.
abstract: In this paper we compare Multi-Layer Perceptrons (a neural network type) with Multivariate Linear Regression in predicting birthweight from nine perinatal variables which are thought to be related. Results show, that seven of the nine variables, i.e., gestational age, mother's body-mass index (BMI), sex of the baby, mother's height, smoking, parity and gravidity, are related to birthweight. We found no significant relationship between birthweight and each of the two variables, i.e., maternal age and social class.
authors.affiliation: Department of Obstetrics and Gynaecology, University of Cambridge, UK.
authors.forename: R J
authors.initials: RJ
authors.lastname: Lapeer
authors.forename: K J
authors.initials: KJ
authors.lastname: Dalton
authors.forename: R W
authors.initials: RW
authors.lastname: Prager
authors.forename: J J
authors.initials: JJ
authors.lastname: Forsström
authors.forename: H K
authors.initials: HK
authors.lastname: Selbmann
authors.forename: R
authors.initials: R
authors.lastname: Derom
mesh_terms: D000367:Age Factors; D001724:Birth Weight; D003658:Decision Making, Computer-Assisted; D005260:Female; D006801:Humans; D016014:Linear Models; D008297:Male; D008954:Models, Biological; D015999:Multivariate Analysis; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D012737:Sex Factors