Establishing the risk of neonatal mortality using a fuzzy predictive model

The objective of this study was to develop a fuzzy model to estimate the possibility of neonatal mortality. A computing model was built, based on the fuzziness of the following variables: newborn birth weight, gestational age at delivery, Apgar score, and previous report of stillbirth. The inference used was Mamdani's method and the output was the risk of neonatal death given as a percentage. 24 rules were created according to the inputs. The validation model used a real data file with records from a Brazilian city. The receiver operating characteristic (ROC) curve was used to estimate the accuracy of the model, while average risks were compared using the Student t test. MATLAB 6.5 software was used to build the model. The average risks were smaller in survivor newborn (p < 0.001). The accuracy of the model was 0.90. The higher accuracy occurred with risk below 25%, corresponding to 0.70 in respect to sensitivity, 0.98 specificity, 0.99 negative predictive value and 0.22 positive predictive value. The model showed a good accuracy, as well as a good negative predictive value and could be used in general hospitals

Adaptive neuro fuzzy inference system · Apgar score · Birth weight · Environmental health · Fuzzy control system · Fuzzy inference system · Fuzzy logic · Gestational age · Infant mortality · Neonatal mortality · Predictive value · Pregnancy · Receiver operating characteristic · Statistics · Artificial Intelligence · Child Nutrition and Water Access · Computer Science · Global Maternal and Child Health · Internal Medicine · Maternal and Neonatal Healthcare · Mathematics · Medicine

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