Predicting the need of Neonatal Resuscitation using Data Mining

It is estimated that approximately 10% of newborns require some kind of assistance for breathing at birth. Aiming to prevent neonatal mortality, the goal behind this paper is to predict the need for neonatal resuscitation given some health conditions of both the newborn and the mother, and also the characteristics of the pregnancy and the delivery using Data Mining (DM) models induced with classification techniques. During the DM process, the CRISP-DM Methodology was followed and the WEKA software tool was used to induce the DM models. For some models, it was possible to achieve sensitivity results higher than 90% and specificity and accuracy results superior to 98%, which were considered to be satisfactory.