This paper builds predictive models for the incidences of neonatal and infant mortality using multiple parametric and non-parametric Machine Learning (ML) techniques. The consensus of the top predictors from the interpret-able ML algorithms (that we use) serve as leading indicators of neonatal and infant mortality and enables us to identify a ‘high-mortality risk’ group of mothers and infants using a household survey data from India. Given the imbalance nature of the data (‘rare-event’ problem) as robustness check we use additional ML methods that are tailor-made for the purpose. The identification of this at-risk population is an important aspect of the government of India’s ‘India Newborn Action Plan’ which has been launched as part of World Health Organization’s Global ‘Every Newborn Action Plan’ and is particularly useful for the creation of targeted public health policies. In addition to identifying some socio-economic, health and behavioral characteristics/predictors of the target group, our analysis also sheds lights on some policy relevance.

 

Keywords: Machine Learning, Prediction Accuracy, Infant Mortality, Public Health Policy

 

JEL Classification: C52, C53, I15, O15