Classification model for cardiotocographies

Cardiotocography is a diagnostic exam performed from the 28th week of pregnancy that registers the fetus cardiac frequency and uterine contractions. From this exam results a cardiotocogram whose reading and observation of the patterns contained in it allow an evaluation of the baby's condition and the fetal vitality in the maternal womb. This work aims the creation of a classification model using Learning Algorithms/Data Mining using the tool Rapid Miner. The subject of study was a Data Set with information registered from a total of 2126 cardiotograms, with 23 attributes, properly classified by 3 specialized obstetricians as to the baby status, in three possible states, namely: N = Normal; S = Suspect; P = Pathologic. All models tested showed an overall accuracy greater than 80%. Therefore the usefulness of creating predictive models for the classification of this type of diagnosis is great.