  Most pregnancies and births result in a good outcome, but complications are
not uncommon and when they do occur, they can be associated with serious
implications for mothers and babies. Predictive modeling has the potential to
improve outcomes through better understanding of risk factors, heightened
surveillance, and more timely and appropriate interventions, thereby helping
obstetricians deliver better care. For three types of complications we identify
and study the most important risk factors using Explainable Boosting Machine
(EBM), a glass box model, in order to gain intelligibility: (i) Severe Maternal
Morbidity (SMM), (ii) shoulder dystocia, and (iii) preterm preeclampsia. While
using the interpretability of EBM's to reveal surprising insights into the
features contributing to risk, our experiments show EBMs match the accuracy of
other black-box ML methods such as deep neural nets and random forests.
