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 selforganizing
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. The resultant SOM model had low quantization error. The study
concluded that Kohonen self-organizing maps provide effective classification models to study asthma
outcomes, particularly when using multidimensional data. SO2 was concluded to be an important
pollutant that requires strict regulation, particularly where frail subpopulations such as the elderly
may be at risk.
