Background and Objectives: Patent ductus arteriosus (PDA) is common in newborns, being associated with high morbidity and mortality. While maternal and neonatal conditions are known contributors, few studies use advanced machine learning (ML) as predictive factors. This study assessed how maternal pathologies, medications, and neonatal factors affect the risk of PDA using traditional statistics and ML algorithms: Random Forest (RF) and XGBoost (XGB). Materials and Methods: A retrospective 3-year cohort study of 201 NICU neonates assessed maternal and neonatal factors. Logistic regression (LR) and chi-square analyses identified significant predictors, while ML models enhanced predictive accuracy and pinpointed key PDA factors. Results: LR identified prolonged rupture of membranes (>18 h) as the most significant predictor (OR: 13.03, p < 0.001). The ML models identified gestational age, maternal anemia, prenatal care level, birth weight, prolonged rupture of membranes, medication usage, diabetes, pregnancy-induced hypertension, SARS-CoV-2 infection, and cervical cerclage as key predictors. The RF model had 76.3% accuracy, moderate sensitivity (47.4%), and high specificity (90%). XGB performed better with 81.4% accuracy, an AUC of 0.872, sensitivity of 92.5%, and specificity of 57.9%. Conclusions: This study shows that maternal and neonatal factors significantly influence the risk of PDA. ML, particularly XGBoost, enhances predictive abilities, guiding targeted interventions and improving neonatal outcomes.

Patent ductus arteriosus (PDA) is common in newborns, being associated with high morbidity and mortality. While maternal and neonatal conditions are known contributors, few studies use advanced machine learning (ML) as predictive factors. This study assessed how maternal pathologies, medications, and neonatal factors affect the risk of PDA using traditional statistics and ML algorithms: Random Forest (RF) and XGBoost (XGB). A retrospective 3 year cohort study of 201 NICU neonates assessed maternal and neonatal factors. Logistic regression (LR) and chi-square analyses identified significant predictors, while ML models enhanced predictive accuracy and pinpointed key PDA factors. LR identified prolonged rupture of membranes (>18 hours) as the most significant predictor (OR: 13.03, p<0.001). The ML models identified gestational age, maternal anemia, prenatal care level, birth weight, prolonged rupture of membranes, medication usage, diabetes, pregnancy-induced hypertension, SARS-CoV-2 infection, and cervical cerclage as key predictors. The RF model had 76.3% accuracy, moderate sensitivity (47.4%), and high specificity (90%). XGB performed better with 81.4% accuracy, an AUC of 0.872, sensitivity of 92.5%, and specificity of 57.9%. This study shows that maternal and neonatal factors significantly influence the risk of PDA. ML, particularly XGBoost, enhances predictive abilities, guiding targeted interventions and improving neonatal outcomes.