A Machine Learning-Based Study of the Effects of Air Pollution and Weather in Respiratory Disease Patients Visiting Emergency Departments in Seoul, Korea using the National Emergency Department Information System

Introduction To date, investigating respiratory disease patients visiting the emergency departments related with fined dust are limited. This study aimed to analyze the effects of two variables?weather and air pollution?on respiratory disease patients who visited emergency departments. Material & Method This study utilized the National Emergency Department Information System (NEDIS) database. The meteorological data were obtained from the National Climate Data Service. Each weather factor reflected the accumulated data of 4 days: a patient's visit day and 3 days before the visit day. We utilized the RandomForestRegressor of Scikit-learn for data analysis. Result The study included 525,579 participants. This study found that multiple variables of weather and air pollution influenced the respiratory diseases of patients who visited emergency departments. Most of the respiratory disease patients had acute upper respiratory infections [J00-J06], influenza [J09-J11], and pneumonia [J12-J18], on which PM10 following temperature and steam pressure was most influential. As the top three leading causes of admission to the emergency department, pneumonia [J12-J18], acute upper respiratory infections [J00?J06], and chronic lower respiratory diseases [J40-J47] were highly influenced by PM10. Conclusion Given the results, among air pollution variables, PM10 influenced the respiratory disease patients’ visits to the emergency departments. It is expected that the number of respiratory disease patients visiting the emergency departments will increase by day 3 when the steam pressure and temperature values are low and the variables of air pollution are high.