Enhancing particulate matter risk assessment with novel machine learning-driven toxicity threshold prediction

Abstract

Airborne particulate matter (PM) poses significant health risks, necessitating accurate toxicity threshold determination for effective risk assessment. This study introduces a novel machine-learning (ML) approach to predict PM toxicity thresholds and identify the key physico-chemical and exposure characteristics. Five machine learning algorithms — logistic regression, support vector classifier, decision tree, random forest, and extreme gradient boosting — were employed to develop predictive models using a comprehensive dataset from existing studies. We developed models using the initial dataset and a class weight approach to address data imbalance. For the imbalanced data, the Random Forest classifier outperformed others with 87% accuracy, 81% recall, and the fewest false negatives (23). In the class weight approach, the Support Vector Classifier minimized false negatives (21), while the Random Forest model achieved superior overall performance with 86% accuracy, 80% recall, and an F1-score of 82%. Furthermore, eXplainable Artificial Intelligence (XAI) techniques, specifically SHAP (SHapley Additive exPlanations) values, were utilized to quantify feature contributions to predictions, offering insights beyond traditional laboratory approaches. This study represents the first application of machine learning for predicting PM toxicity thresholds, providing a robust tool for health risk assessment. The proposed methodology offers a time- and cost-effective alternative to classical laboratory tests, potentially revolutionizing PM toxicity threshold determination in scientific and epidemiological research. This innovative approach has significant implications for shaping regulatory policies and designing targeted interventions to mitigate health risks associated with airborne PM.