Machine learning algorithms and computational validation of single-nucleotide polymorphisms of antioxidant enzymes and oxidative stress markers in neonates.

Aim: To evaluate machine learning algorithms (MLAs) for predicting factors (oxidative stress biomarkers [OSBs] and single-nucleotide polymorphism of the antioxidant enzymes) for respiratory distress syndrome (RDS) and significant alterations in the liver functions (SALVs). Materials & methods: MLAs were applied for predicting the RDS and SALV (with OSB and single-nucleotide polymorphisms in the antioxidant enzymes) with area under the curve (AUC) as the accuracy measure.

Results

The C5.0 algorithm best predicted SALV (AUC: 0.63) with catalase as the most important predictor. Bayesian network best predicted RDS (AUC: 0.6) and ENOS1 was the most important predictor.

Conclusion

MLAs carry great potential in identifying the potential genetic and OSBs in neonatal RDS and SALV. Validation in prospective studies is needed urgently.