A systematic review of data mining and machine learning for air pollution epidemiology.
Journal:
BMC public health
Published Date:
Nov 28, 2017
Abstract
BACKGROUND: Data measuring airborne pollutants, public health and environmental factors are increasingly being stored and merged. These big datasets offer great potential, but also challenge traditional epidemiological methods. This has motivated the exploration of alternative methods to make predictions, find patterns and extract information. To this end, data mining and machine learning algorithms are increasingly being applied to air pollution epidemiology.