An Intelligent Air Monitoring System For Pollution Prediction: A Predictive Healthcare Perspective.

This article focuses on a novel Internet of Things (IoT)-based framework for real-time monitoring, detection, and prediction of health vulnerabilities caused by air pollution. The proposed four-layered model integrates IoT sensors, fog computing via the FogBus platform, and a Differential Evolution-Recurrent Neural Network (DE-RNN) to classify health-impacting air particles, analyze temporal health adversity probabilities (HAP), and predict the Air Impact on Health (AIH). The system employs Bayesian Belief Networks for data classification and Self-Organized Mapping (SOM) for visualizing health vulnerability, with data collected from both direct healthcare devices and environmental sensors. Experimental validation using a dataset of over 60,000 instances from the University of California, Irvine repository demonstrated that the model outperforms state-of-the-art classifiers and predictive techniques in accuracy, sensitivity, specificity, reliability, and stability, suggesting its potential for enhancing smart healthcare environments through timely air quality assessment and health risk prediction.