A Hybrid Machine Learning Approach for Early Risk Prediction of Preterm Birth Using Contraction Pattern

: Preterm birth means delivery of baby before 37th week of gestation which can cause severe life challenges both to the mother and the baby. The condition has been linked to a range of prolonged complications such as respiratory distress, infection and congenital malformations. Estimating the risk of preterm birth accurately is a formidable challenge in the practice of obstetrics given the many causative risk-factors. However, classifying a pregnancy as high-risk enables early medical interventions to enhance neonatal outcomes. This study investigates the machine learning algorithms prediction (Support Vector Machine (SVM), Random Forest, and XGBoost) of risk of preterm birth. Models were trained on a representative subset of maternal and clinical factors and validated on accuracy, F1-score, recall, and precision. Here are some of the advantages of machine learning in healthcare been discovered. Preterm birth is the most predictable event. The best model was the stacking SVM with XGBoost and Random Forest. Using various algorithms in a stacking model, the prediction accuracy was increased overall. The model allows the combination of models and therefore improves predictability compared to the use of a single algorithm. These results reinforce a growing role for machine learning in obstetrics through better risk assessing, predictive accuracy, and dealing with uncertainty. Finally, this research contributes to the development of predictive models which can be used by health care providers to allow for early interventions and improve maternal and new born health.