Methylation Data of Parents in the Prediction of a Preterm Birth: A Machine Learning Approach

Preterm birth is a serious issue which can affect the whole family, especially the mother both physically and mentally. Further, babies also need to face a lot of short-term and long-term complications, sometimes throughout their life. Annually we have approximately 15 million premature babies worldwide, which is the leading cause of death among children. However, early prediction of a preterm birth can help the clinician to give proper treatments to the mother in order to avoid this complication. Previous biological studies showed that there are epigenetic differences between a preterm baby and a full-term baby, and associations between prenatal risk factors and epigenetic changes. Anyhow it is comparably very hard to get epigenetic data from an infant before labor. Hence, this study analyses the methylation data of father and mother individually in the prediction of the possibility of premature birth using machine learning algorithms such as random forest, support vector machine and K-nearest neighbor. As we have high number of features in this data, mutual information is used to select the relevant features of this study. Different number of features are selected using mutual information and their performances are evaluated using all the three machine learning algorithms to reveal the best number of features. Results indicate that top 15 methylation features taken from the father along with random forest classifier outperform other models with a perfect accuracy (AUC = 1 ± 0.00) in the prediction of premature possibilities.