Alif, Meheruba HasinMain Bhuyan, FarahTajrin, Radhika2026-08-042026-08-042024-01-01M. H. Alif, F. Main Bhuyan and R. Tajrin, "Predicting Preterm Birth Among South-Asian Women Using Machine Learning," 2024 IEEE International Conference on Computing, Applications and Systems (COMPAS), Cox's Bazar, Bangladesh, 2024, pp. 1-6, doi: 10.1109/COMPAS60761.2024.10796565.97983315297652-s2.0-85215530445https://hdl.handle.net/10361/28790Preterm 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.6 Pagesen-USData analysisDecision treeFeature extractionLogistic regressionMachine learningMultilayer perceptronNeural networkPreterm birthRandom forestPregnancy--Complications.Deep learning (Machine learning).Conference Proceeding10.1109/COMPAS60761.2024.10796565