Predicting Premature Birth in Pregnant Women via Assisted Reproductive Technologies using Neural Network

Nowadays, assisted reproductive technologies are widely used to treat infertility in couples. Studies indicate that the rate of premature birth after using Assisted Reproductive Technologies has been increased as compared to normal pregnancies. The purpose of our study was predicting premature birth in pregnant women via Assisted Reproductive Technologies using artificial neural networks. In this retrospective study, initially 45 variables were identified as effective factors for prediction of premature birth in pregnant women via Assisted Reproductive Technologies and data of 130 women were extracted using clinical records in Sarem hospital in Tehran from 1998 to 2014 in October and November, 2014. The most important variables were identified as effective variables using feature selection algorithm and decision tree in SPSS Clementine. Multi-Layer Perceptron network was designed to predict the premature birth in Matlab software. Confusion matrix was used for evaluation in order to calculate accuracy, sensitivity and specificity. We identified fifteen effective features using feature selection algorithm and decision tree as inputs of the neural networks. Multi-Layer Perceptron network was designed and evaluated. The accuracy, sensitivity and specificity of the test data were 87.2%, 80.0% and 88.2%, respectively and for the total data were 95.4%, 95.0% and 95.5%, respectively. According to the results, designed neural network for predicting premature birth in pregnant women via Assisted Reproductive Technologies can be helpful in prevention of premature birth complications.