title: Risk groups in children under six months of age using self-organizing maps.
abstract: Fetal and infant growth tends to follow irregular patterns and, particularly in developing countries, these patterns are greatly influenced by unfavorable living conditions and interactions with complications during pregnancy. The aim of this study was to identify groups of children with different risk profiles for growth development. The study sample comprised 496 girls and 508 boys under six months of age from 27 pediatric primary health care units in the city of Rio de Janeiro, Brazil. Data were obtained through interviews with the mothers and by reviewing each child's health card. An unsupervised learning, know as a self-organizing map (SOM) and a K-means algorithm were used for cluster analysis to identify groups of children. Four groups of infants were identified. The first (139) consisted of infants born exclusively by cesarean delivery, and their mothers were exclusively multiparous; the highest prevalences of prematurity and low birthweight, a high prevalence of exclusive breastfeeding and a low proportion of hospitalization were observed for this group. The second (247 infants) and the third (298 infants) groups had the best and worst perinatal and infant health indicators, respectively. The infants of the fourth group (318) were born heavier, had a low prevalence of exclusive breastfeeding, and had a higher rate of hospitalization. Using a SOM, it was possible to identify children with common features, although no differences between groups were found with respect to the adequacy of postnatal weight. Pregnant women and children with characteristics similar to those of group 3 require early intervention and more attention in public policy.
authors.affiliation: PEB/UFRJ, Rio de Janeiro, Brazil. Electronic address: arthur@peb.ufrj.br.
authors.forename: A O C
authors.initials: AO
authors.lastname: Schilithz
authors.affiliation: IESC/UFRJ, Rio de Janeiro, Brazil.
authors.forename: P L
authors.initials: PL
authors.lastname: Kale
authors.affiliation: ENSP/FIOCRUZ, Rio de Janeiro, Brazil.
authors.forename: S G N
authors.initials: SG
authors.lastname: Gama
authors.affiliation: PEB/UFRJ, Rio de Janeiro, Brazil.
authors.forename: F F
authors.initials: FF
authors.lastname: Nobre
mesh_terms: D000465:Algorithms; D001942:Breast Feeding; D002657:Child Development; D016000:Cluster Analysis; D003430:Cross-Sectional Studies; D003906:Developing Countries; D005260:Female; D006130:Growth Disorders / Q000175:diagnosis*; D006305:Health Status Indicators; D006760:Hospitalization; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D007234:Infant, Premature / Q000254:growth & development*; D008297:Male; D015233:Models, Statistical; D016571:Neural Networks, Computer; D011247:Pregnancy; D015995:Prevalence; D012306:Risk; D012984:Software
keywords: Artificial neural network; Cesarean delivery; Exclusive breastfeeding; Infant growth; Preterm birth; Self-organizing maps