We have developed models to predict the incidence of atopic dermatitis using regression analysis and artifi cial neural networks

(ANN). Initially, the prediction models were created using various inputs, including air pollutants (SO 2 , CO, O 3 , NO 2 ,

and PM 10 ), meteorological factors (temperature, humidity, wind speed, and precipitation), population rates, and clinical data

from South Korea, referred to as the average model. Subsequently, we developed models that use sex and age as variables

instead of population rates, named the sex and age model. Both sets of models were designed to forecast incidence rates on

a nationwide scale (NW), as well as for 16 administrative districts (AD) in South Korea, which includes seven metropolitan

areas and nine provinces. We found that SO 2 signifi cantly aff ected the incidence rate, and the inclusion of regional variables

in the AD models helped account for regional variations in incidence rates. The average models generally provided accurate

predictions of incidence rates, with SO 2 chosen as the key independent variable in the regression models for the fi ve air

pollutants studied. The R 2 values for the average models using regression are 0.70 for the NW model and 0.89 for the AD

model. Among the ANN-based models, the R 2 values are 0.84 for the NW model and 0.90 for the AD model, this indicated

a slightly higher predictive accuracy. For the sex and age models, we diff erentiated between children under 10 years of age

and those older. In these models, ANN demonstrated greater accuracy than regression, with R 2 values of 0.95, 0.92, 0.96, and

0.92 for the sex and age NW model under 10 years old, sex and age AD model under 10 years old, sex and age NW model

over 10 years old, and sex and age AD model over 10 years old, respectively.