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Predicting Premature Birth Risk With Cfrna, Jason Lin, Jonathan Marin, John Santerre

SMU Data Science Review

Identifying which genes are early indicators for preterm births using cell-free ribonucleic acid (cfRNA) from non-invasive blood tests provided by pregnant women can improve prenatal care. Currently, there are no medical tests for early detection of preterm birth risk in routine checkups for pregnant women. Recent studies have shown potential genes that can predict preterm birth. Machine learning techniques are utilized to see if the Area Under the Curve (AUC) can be improved upon when evaluating the prediction accuracy for chosen genes sequences and concentrations. Using cell-free RNA data from non-invasive blood tests in conjunction with machine learning, we improve …