Infant Mortality Prediction using Birth Certificate Data
This work addresses infant mortality prediction for public health researchers, but it is incremental as it applies existing methods to new data subsets.
The paper tackled infant mortality prediction using birth certificate data, focusing on feature importance across racial subsets and comparing race-specific models to general ones, with results showing their methodology outperforms standard classification methods used in epidemiology.
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.