  The Infant Mortality Rate (IMR) is the number of infants per 1000 that do not
survive until their first birthday. It is an important metric providing
information about infant health but it also measures the society's general
health status. Despite the high level of prosperity in the U.S.A., the
country's IMR is higher than that of many other developed countries.
Additionally, the U.S.A. exhibits persistent inequalities in the IMR across
different racial and ethnic groups. In this paper, we study the infant
mortality prediction using features extracted from birth certificates. We are
interested in training classification models to decide whether an infant will
survive or not. We focus on exploring and understanding the importance of
features in subsets of the population; we compare models trained for individual
races to general models. Our evaluation shows that our methodology outperforms
standard classification methods used by epidemiology researchers.
