Air Pollution and the Monitoring of Environmental Health Compared with Logistic Regression (LR) and Random Forest (RF) Algorithms

Nowadays, nine out of ten people inhale polluted air, causing dangerous health concerns. This means that air pollution poses a serious threat to society's health. It supports enhanced dimension, cause detection, prediction, expectation, and logical problem-solving. AI technology can rapidly and accurately detect air pollution. AI has quickly exposed the extent of air pollution. This study estimates logistic regression (LR) and Random Forest (RF) models, two widely used statistical methods for predicting long-term air pollution and environmental health. Logistic regression may predict air pollution more effectively than other machine learning approaches. The objective of this analysis is to improve the algorithm's performance during the collection activity and reduce air pollution. The average detection accuracy falls within one standard deviation, indicating that the proposed model is as efficient as, and more effective than, the modern method. Logistic Regression and Random Forest (which is valued the highest accuracy (0.93) and precision (0.92))