Exploring nonlinear effects of air pollution on hospital admissions by disease using gradient boosting machines

Air pollution has been linked to premature mortality and reduced life expectancy, with acute and chronic effects on human health. These effects can be difficult to measure because of possible interactions and nonlinear relationships with other variables such as age, weight, sex, and socioeconomic status.Multi-dimensional relationships are difficult to model using conventional statistical methods. However, modern machine learning techniques have been quite successful in this domain.In this study, gradient boosting regression trees are used to predict the severity/mortality of the leading causes of hospitalization in Mexico City for 91,964 patients during the years 2015-2020 to measure the impact due to different air pollutants. The results show multiple nonlinear relationships and a significant effect of air pollutants on some of the most prevalent diseases.

I. Introduction

Predictive analytics play an important role in clinical research. Conventionally, predictive analytics is performed using parametric modeling which comes with a number of assumptions. For example, generalized linear regression models require linearity and additivity to hold for the underlying data. However, these assumptions may not hold in practice [22].