Machine learning reveals distinct temperature thresholds and environmental modulators for atopic dermatitis and allergic contact dermatitis prevalence in South Korea.
Atopic dermatitis prevalence rises with air pollution above 17.4°C monthly temperature.
Machine learning reveals distinct temperature thresholds and environmental modulators for atopic dermatitis and allergic contact dermatitis prevalence in South Korea.
Atopic dermatitis (atopic dermatitis) and allergic contact dermatitis (allergic contact dermatitis) are common inflammatory skin diseases influenced by environmental factors, but disease-specific environmental pathways remain poorly defined.
This study developed a machine learning model to predict monthly disease prevalence and characterize distinct environmental conditions associated with each disease.
We analyzed nationwide health insurance claims data for atopic dermatitis, allergic contact dermatitis, and corns (control) from six major South Korean cities from 2012 to 2017, constituting 432 city-month records per disease.
The M5P model tree algorithm predicted relative monthly prevalence based on meteorological data (temperature, humidity, precipitation, diurnal temperature range) and air pollutants (SO₂, NO₂, CO, PM10), with performance evaluated using Pearson Correlation Coefficient (coefficient) and Mean Absolute Error (Mean Absolute Error).
Analysis of 3,990,692 atopic dermatitis and 16,890,182 allergic contact dermatitis cases showed that the combined weather-pollution model achieved high accuracy for atopic dermatitis (coefficient = 0.839, Mean Absolute Error = 0.038) and allergic contact dermatitis (coefficient = 0.932, Mean Absolute Error = 0.049).
This data-driven approach provides insights into disease-specific environmental triggers for public health interventions.