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

Moses Mogakolodi Kebalepile, Loveness Nyaradzo Dzikiti and Kuku Voyi
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Moses Mogakolodi Kebalepile: Faculty of Health Sciences, School of Health Systems and Public Health, University of Pretoria, Pretoria 0083, South Africa
Loveness Nyaradzo Dzikiti: Faculty of Health Sciences, School of Health Systems and Public Health, Ross University School of Veterinary Medicine, Basseterre, Saint Kitts and Nevis
Kuku Voyi: Faculty of Health Sciences, School of Health Systems and Public Health, University of Pretoria, Pretoria 0083, South Africa

IJERPH, 2021, vol. 18, issue 21, 1-10

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. 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. SO 2 was concluded to be an important pollutant that requires strict regulation, particularly where frail subpopulations such as the elderly may be at risk.

Keywords: self-organizing maps; classification model; air quality; asthma outcomes; asthma research; artificial neural networks (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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