The Influence of Air Pollution on Pulmonary Disease Incidence Analyzed Based on Grey Correlation Analysis

Air pollution is a primary health threat issue worldwide because it is closely concerned with respiratory diseases. A random survey reported that around 7 million people died because of ambient and household air pollution. Especially, the people suffering from asthma and chronic obstructive pulmonary disease (COPD) are highly affected by air pollutants. The air pollution components induce asthma onset and COPD acute exacerbation, which leads to maximized mortality and morbidity rate. Therefore, the influence of air pollution on COPD should be examined continuously to minimize the mortality rate. Several methods are presented in this field to investigate the relationship between health and pollutants. However, the existing approaches are only predicting the short-term data and have difficulties such as computation time, redundant data in large data analysis, and data continuity. Then, this research introduced the meta-heuristic optimized grey correlation analysis (MH-GCA) to solve the research difficulties. The correlation analysis has several models that identify the relationship between the pollution factors with COPD disease. The method analysis of the particulate matter (〖PM〗_10) in air pollution is more relevant to COPD and lung cancer disease. The grey analysis uses the uncertainty concept to identify the particle influence on air pollution. In the analysis, the cuttlefish optimization algorithm was applied to select more relevant features from the pollutant list that reduces the computation time and correlation analysis rate. The introduced system was evaluated using the air quality dataset and COPD dataset developed with the help of the MATLAB tool. The system increases the influence recognition accuracy (2.48%) and MCC (3.11%) and decreases the error rate (55.89%) for different pollutants.