The C5.0 algorithm achieved an AUC of 0.63 for predicting SALV, with catalase identified as the most significant predictor. The Bayesian network had an AUC of 0.60 for predicting RDS, with ENOS1 being the most important predictor. Machine learning algorithms showed potential in identifying genetic markers and oxidative stress biomarkers associated with neonatal RDS and liver function alterations. Further validation in prospective studies is needed. This evidence concerns the gene CAT and newborn respiratory distress syndrome.