This study aims to identify patterns in maternal and fetal characteristics in the prediction of infant mortality by incorporating innovative techniques like data mining, with proven relevance for public health. A database was developed with infant deaths from 2000 to 2004 analyzed by the Committees for the Prevention of Infant Mortality, based on integration of the Information System on Live Births (SINASC), Mortality Information System, and Investigation of Infant Mortality in the State of Paraná. The data mining software was WEKA (open source). The data mining conducts a database search and provides rules to be analyzed to transform the data into useful information. After mining, 4,230 rules were selected: teenage pregnancy plus birth weight < 2,500g, or post-term birth plus teenage mother with a previous child or intercurrent conditions increase the risk of neonatal death. The results highlight the need for greater attention to teenage mothers, newborns with birth weight < 2,500g, post-term neonates, and infants of mothers with intercurrent conditions, thus corroborating other studies.
Database; Infant Mortality; Information Systems; Artificial Intelligence; Epidemiologic Surveillance
