ỨNG DỤNG AI ĐỂ GIÁM SÁT VÀ DỰ ĐOÁN CHẤT LƯỢNG KHÔNG KHÍ TRONG PHÒNG BỆNH

Abstract

This study develops an indoor air quality monitoring and prediction system for hospital rooms based on the Internet of Things (IoT) and Artificial Intelligence (AI). The system employs environmental sensors to collect real-time data, including temperature, humidity, CO₂ concentration, PM2.5 particulate matter, and mixed gas levels. After preprocessing, the collected data are fed into the XGBoost machine learning model to predict trends in air quality changes. The prediction results are displayed on an IoT platform, and the system is capable of issuing alerts when environmental parameters exceed safety thresholds. Experimental results demonstrate that the system operates reliably and that the prediction model achieves satisfactory accuracy, effectively supporting air quality assessment. The study confirms the feasibility of integrating AI and IoT for monitoring hospital room environments and highlights its potential application in other enclosed spaces such as hospitals, schools, and offices.