Symptom-Based Prediction of Respiratory Diseases Risk in Relation to Air Pollution Exposure

With over half of the world's population exposed to rising air pollution levels, the risk of lung damage and diseases like interstitial lung disease (ILD), pulmonary sarcoidosis, and chronic obstructive pulmonary disease (COPD) increases. Early detection significantly enhances survival and recovery rates, yet research using air quality data for lung disease prediction in the U.S. is scarce. This study aims to develop a predictive model for lung disease incidence based on particulate matter (PM2.5) and ozone levels in the United States, promoting more frequent screenings and early detection. We used daily PM2.5 and ozone measurements from the Environmental Protection Agency (EPA) alongside interstitial lung disease and COPD data for 2000, 2005, 2010, and 2014. Machine learning techniques were employed for data preparation, analysis, model building, and training. Preprocessing steps included merging and cleaning datasets to ensure data quality and robustness. The Spatial Lag Model (SLM), Spatial Error Model (SEM), and Geographically Weighted Regression (GWR) were used to analyze the spatial-temporal dependencies between PM2.5, ozone, and lung disease mortality. County-specific latitude and longitude measures were used to build the GWR. Preliminary results confirmed a significant correlation between air pollutant levels and lung disease incidence. Our machine learning models effectively identified patterns and trends that can be leveraged for early prediction of lung disease cases. The findings underscore the potential of utilizing air quality data for predicting lung disease incidence, facilitating early diagnosis and treatment. This research provides valuable insights into the health impacts of air pollution and highlights the necessity for further exploration in this area.