A Health-Promoting Warning System for Outdoor Workers Based on Regularized Rotation Forests and Air Quality Deterioration

다국어 초록 (Multilingual Abstract)

This study aims to develop a health-promoting warning system for outdoor workers by predicting air quality levels using the Regularized Rotation Forest model. Utilizing a dataset from Kaggle's Air Quality and Pollution Assessment, the study evaluates the effectiveness of the air quality prediction model, emphasizing the significance of PM2.5, PM10, Temperature, NO2, and CO. The findings reveal that the proposed model demonstrates high accuracy and predictive power, contributing to reducing health risks by providing real-time alerts to outdoor workers. This research provides scientific evidence for improving air quality management and environmental policies and suggests further development of the model through data expansion across diverse regions and conditions and the integration of additional variables.