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.

本文旨在通过分析出生证明上所提取的特征，研究婴儿死亡率的预测问题。我们关注于训练分类模型来决定婴儿的存活与否，并比较不同种族之间的差异。我们的评估表明，该方法胜过流行病学研究中所采用的标准分类方法。

使用出生证明数据预测婴儿死亡率