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

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. 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.

Automated summary

This study used self-organizing maps (SOM) to explore a computational intelligence paradigm for asthma research, combining air quality, clinical data, and socio-demographic data for classification. Age was identified as a more important factor, with older patients more likely to have asthma. The study concluded that self-organizing maps provide effective classification models in studying asthma outcomes, especially when using multidimensional data.