Optimized Machine Learning with TPE for Air Quality Classification and Public Health Risk Estimation - Ayun Hapsari (Journal of Artificial Intelligence and Legal Technology, 2025)
Air pollution in rapidly urbanizing cities such as Delhi poses a critical threat to public health due to the persistent exceedance of safe thresholds for particulate matter and gaseous pollutants. Accurate air quality classification and timely health risk estimation are essential to support early warning systems and guide urban policy interventions. This study develops a multi-class Air Quality Index (AQI) classification framework using Logistic Regression, Random Forest, Decision Tree, Support Vector Classifier (SVC), K-Nearest Neighbors (KNN), and Gradient Boosting, applied to a comprehensive dataset of daily pollutant concentrations (PM2.5, PM10, NO₂, SO₂, CO, and O₃) and meteorological parameters from Delhi. Data preprocessing included outlier removal, feature scaling, and label encoding of AQI categories, followed by an 80:20 train-test split to ensure robust model evaluation. Model performance was assessed using Accuracy, Precision, Recall, and F1-score. The experimental results show that ensemble and kernel-based models achieved the highest predictive accuracy, with Random Forest reaching an accuracy of 0.7611 and an F1-score of 0.7522, followed closely by Decision Tree and Gradient Boosting with F1-scores above 0.74. Logistic Regression and SVC maintained moderate yet consistent performance, while KNN was more sensitive to data distribution, achieving an F1-score of 0.605. Confusion matrix analysis revealed that misclassifications were mostly confined to adjacent AQI categories, reflecting the natural difficulty of distinguishing borderline pollution levels. The novelty of this study lies in integrating multi-class AQI classification with a structured machine learning framework capable of mapping environmental conditions directly to health risk levels. By aligning predictions with WHO and US-EPA thresholds, the framework facilitates actionable insights for public health authorities, enabling the design of early warning systems and targeted interventions for vulnerable populations. These findings advance the technical landscape of urban air quality management and provide a scalable foundation for health-oriented environmental decision-making in highly polluted megacities.