Predicting preterm birth among south-asian women using machine learning

Abstract

Preterm Birth remains one of the preventable yet large contributors to child and maternal mortality. Despite progress in reducing infant mortality, South Asia continues to report a significant number of preterm birth-related deaths annually. Moreover, those who survive have to bear the long-term impact of physical and neurological disabilities - especially low-income households who are unable to afford healthcare. This study aims to offer a prediction model that can predict preterm birth using social, physical and health records of South-Asian women. Traditional models such as Decision Trees, Random Forest, Support Vector Machines, Logistic Regression and neural network-based deep learning models such as Multilayer Perceptron are used to compare the models' AUC, F1 scores and accuracy points. Random Forest generated the highest accuracy and F1 score (89%), whereas Multilayer Perceptron generated the best AUC score (88%) and had an overall consistent performance. Further analysis revealed the main factors contributing to preterm birth were weight before pregnancy, age and BMI of the mother.