A Machine Learning-Based Study of the Effects of Air Pollution and Weather in Respiratory Disease Patients Visiting Emergency Departments

Abstract

Background. To date, investigating respiratory disease patients visiting the emergency departments related with fined dust is limited. This study aimed to analyze the effects of two variable-weather and air pollution on respiratory disease patients who visited emergency departments. Methods. 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 the 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. Most of the respiratory patients visiting EDs were diagnosed with acute upper respiratory infections, influenza, and pneumonia. Following temperature, steam pressure and PM10 had influential relations with these diseases. 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. 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.

Respiratory Diseases Prediction from a Novel Chaotic System

“…This was achieved by developing a new chaos equation using patient data and weather predictions that have been gathered. The findings of this study agree with previous studies that have linked weather data to respiratory disease cases (Lee et al 2022;Bhat et al 2021).…”

“…The daily projected patient count and the output of the new chaotic equation had a strong correlation of 90.16% after the chaotic system's equations were solved. As a result, the findings of this study are in line with those of other studies that have evaluated the effectiveness of time series in forecasting the occurrence of respiratory diseases (Shaman and Kohn 2009;Lee et al 2022). This study reported a higher performance measure, with a correlation of 90.16% between real patient cases and predicted data.…”

“…A study by Lee et al (2022) involving 525,579 individuals found that various weather and air quality factors affected the respiratory illnesses of people who visited emergency rooms (Lee et al 2022). The majority of the patients with respiratory diseases had acute upper respiratory infections (J00-J06), influenza (J09-J11), and pneumonia (J12-J18), with PM10 temperature and steam pressure having the greatest effects.…”

Ambient air pollution, temperature and hospital admissions due to respiratory diseases in a cold, industrial city

“…Regarding meteorological factors, previous studies have suggested that temperature, humidity, air pressure and other meteorological conditions affect the incidence of respiratory diseases and the number of medical visits [25,26], and exposure to low and high temperatures increase the risk of disease [27]. In addition, in a previous study, the incidence of respiratory diseases caused by high temperature exposure was higher than that caused by low temperature exposure; however, the incidence of respiratory syncytial virus (RSV) infection was related to low temperature exposure [28].…”

Influenza incidence and air pollution: Findings from a four-year surveillance study of prefecture-level cities in China

“…Moreover, there was a lagged effect of air pollutants on the incidence of influenza. Recent studies on the correlation between the influenza incidence and air pollutants have mostly applied statistical models, such as correlation analysis (30), regression analysis (30), machine learning (31), and non-linear models (32). To the best of our knowledge, few studies have developed and applied sensitivity analysis to examine the relationship between disease and environment.…”