Traffic noise and children's health: New insights from a machine learning algorithm?

Abstract

Studies on the influence of traffic noise on children's health are usually very comprehensive and collect data on a large number of measured variables in comparatively large samples. In the NORAH Study, for example, almost 700 variables have been considered including more than 100 variables related to traffic noise. With a theory-based approach, the statistical evaluation of that data focused on a limited number of variables to be included in the regression models as predictors, mediators, moderators, or confounders. In contrast, machine learning (ML) methods are able to consider the complete scope of variables in an analysis. Random forest models are one type of ML methods for dealing with possible multicollinearity of predictors or nonlinear relationships. Although these methods can offer advantages, they have hardly been used in relation to traffic noise and children's health. In the EU project EqualLife, random forest models are computed in order to obtain information on the significance of individual exposomes (e.g., traffic noise) for children's health. In the present paper, we compare the results of a regression model and a random forest model using the NORAH Study as an example. Possible advantages and disadvantages of the methods are discussed.