As far-fetched as it may seem today, there are a couple of compelling reasons why some humans may one day be born without either a mother or father as we now know them, and with no other humans around to bring them up. The first is the uninhabitable Earth scenario: doomsday. This is the idea that one day our planet will not be able to support human life. This may be due to catastrophic climate change brought on by a large asteroid or comet impact, a nuclear winter following a global nuclear war or a pandemic so severe that humans do not survive. Whatever the cause of our demise, if humans want to ultimately survive and one day re-emerge, it makes sense to store the building blocks of people – ovum and sperm – ready for a resurrection of the human race once our planet is habitable again. There are already gene banks around the world that have been created to store plant seeds for just this kind of eventuality.

This past week, physicians at Children's Hospital in Philadelphia announced that they'd had remarkable success with keeping lamb fetuses alive outside a womb--in a plastic bag filled with warm amniotic fluid, with the fetus' heart circulating blood through a filter to keep it oxygenated. Astonishing pictures of wee unborn laminated lambs quickly spun up the media science-fiction reference engine. Someday, that might be a human baby floating in a next-gen artificial uterus. Talking heads name-checked Gattaca and Brave New World. You could get a whiff of Blade Runner in there.

First things first: while this artificial womb is futuristic as hell, it's not meant to replace a good old-fashioned uterus. Such technology, he and many others argue, would allow for safer, more controlled pregnancies (for both fetuses and mothers) and would take the childbearing onus off of individuals biologically equipped to carry pregnancy. Potential controversies abound, of course. How do abortion laws that hinge on viability--the point at which a fetus could survive outside the womb--change when a fetus could technically survive outside the womb at any point? How do parental rights change?

We describe an application of machine learning to the problem of predicting preterm birth. We conduct a secondary analysis on a clinical trial dataset collected by the National In- stitute of Child Health and Human Development (NICHD) while focusing our attention on predicting different classes of preterm birth. We compare three approaches for deriving predictive models: a support vector machine (SVM) approach with linear and non-linear kernels, logistic regression with different model selection along with a model based on decision rules prescribed by physician experts for prediction of preterm birth. Our approach highlights the pre-processing methods applied to handle the inherent dynamics, noise and gaps in the data and describe techniques used to handle skewed class distributions. Empirical experiments demonstrate significant improvement in predicting preterm birth compared to past work.