Machine learning-based prediction of preterm birth risk using methylation changes in neonatal cord blood CpG sites.

BACKGROUND

Preterm birth, defined as delivery before 37 weeks of gestation, is a major cause of neonatal morbidity and mortality. DNA methylation changes at CpG sites have been associated with the risk of preterm birth.

OBJECTIVE

This study aimed to identify differential CpG sites in cord blood and develop predictive machine learning models based on these methylation changes to assess preterm birth risk.

METHODS

Methylome data from 110 neonatal cord blood samples in the GSE110828 dataset were analyzed to identify CpG sites differing between preterm and full-term births (88 for training, and 22 for testing, respectively). Key CpG sites were selected using Lasso, Elastic Net, and Random Forest. Forty-five predictive models were constructed and evaluated for accuracy, precision, recall, and F1 score.

RESULTS

Sixty-six CpG sites showed significant differences between preterm and full-term groups. Four models, including Random Forest with Lasso and Gradient Boosting with Random Forest, achieved optimal predictive performance, each with a validation accuracy of 93.75%.

CONCLUSION

DNA methylation changes at CpG sites in cord blood are associated with preterm birth risk. CpG-based methylation models demonstrate high predictive accuracy and hold promise for early clinical risk assessment.