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Relating Prenatal Hg Exposure and Neurological Development in Children with Machine Learning

We use machine learning techniques to address a problem from environmental epidemiology. Prenatal exposure to mercury (Hg) can impair the neurological development of children. We study the relations between exposure factors and indices of neurological development of children. To this end, we use predictive modelling approaches for regression, as well as methods for estimating feature importance. While the learned models are insufficient for accurate prediction of neurodevelopment indices, they nevertheless point to the exposure factors that most influence neurological development.