Supervised Kohonen Self-Organizing Maps of Acute Asthma from Air Pollution Exposure.

ABSTRACT: There are unanswered questions with regards to acute respiratory outcomes, particularly asthma, due to environmental exposures. In contribution to asthma research, the current study explored a computational intelligence paradigm of artificial neural networks (ANNs) called self-organizing maps (SOM). To train the SOM, air quality data (nitrogen dioxide, sulphur dioxide and particulate matter), interpolated to geocoded addresses of asthmatics, were used with clinical data to classify asthma outcomes. Socio-demographic data such as age, gender and race were also used to perform the classification by the SOM. All pollutants and demographic traits appeared to be important for the correct classification of asthma outcomes. Age was more important: older patients were more likely to have asthma. T

SUBMITTER: Kebalepile MM 

PROVIDER: S-EPMC8582892 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature