A Neural Network-based Approach to Prediction of Preterm Birth using Non-invasive Tests

Background: One of the main reasons for neonatal deaths is preterm delivery, and infants who have survived preterm birth (PB) are at risk of significant health complications. However, an effective method for reliable and accurate prediction of preterm labor has yet to be proposed. Objective: This study proposes an artificial neural network (ANN)-based approach for early prediction of PB, and consequently can hint physicians to start the treatment earlier, reducing the chance of morbidity and mortality in the infant. Material and Methods: This historical cohort study proposes a feed-forward ANN with ۷ hidden neurons to predict PB. Thirteen risk factors of PB were collected from ۳۰۰ pregnant women (۱۵۰ with preterm delivery and ۱۵۰ normal) as the ANN inputs from ۲۰۱۸ to ۲۰۱۹. From each group, ۷۰%, ۱۵%, and ۱۵% of the subjects were randomly selected for training, validation, and testing of the model, respectively. Results: The ANN achieved an accuracy of ۷۹.۰۳% for the classification of the subjects into two classes normal and PB. Moreover, a sensitivity of ۷۳.۴۵% and specificity of ۸۴.۶۲% were obtained. The advantage of this approach is that the risk factors used for prediction did not require any lab test and were collected in a questionnaire.  Conclusion: The efficacy of the proposed approach for the early identification of pregnant women, who are at high risk of preterm delivery, leads to necessary care and clinical interventions, applied during the pregnancy.

کلیدواژه ها:

Premature birth ، Artificial Neural Network ، Machine Learning ، Preterm Delivery ، Preterm Labor ، Pregnancy