Machine Learning Approaches to Assess the Health Impact of Air Pollution

Abstract

Air pollution is a critical global health concern, linked to a wide range of respiratory, cardiovascular, and chronic diseases. This study investigates the use of machine learning to assess the health impacts of air pollution by leveraging diverse datasets that include air quality indices, meteorological factors, and health outcomes. The research evaluates several algorithms, including Decision Trees, Support Vector Machines, Random Forest, XGBoost, and Tree Ensemble, using performance metrics such as precision, recall, F1 score, and accuracy. Among these, ensemble methods, particularly Tree Ensemble, demonstrated superior generalization capabilities, achieving an accuracy of 97%. The study highlights the ability of machine learning models to capture complex, non-linear relationships between environmental and health variables, offering significant improvements over traditional statistical methods. However, challenges such as data heterogeneity, ethical concerns, and model interpretability remain critical barriers. The research emphasizes the need for incorporating real-time data streams, explainable AI techniques, and fairness mechanisms to enhance model transparency and usability for decision-making. This work underscores the transformative role of machine learning in mitigating the adverse health effects of air pollution. By integrating predictive models with policy frameworks and fostering global collaboration, future studies can drive effective public health interventions and promote sustainable environmental practices.