Identifying the key factors of mercury exposure in residents of southwestern Iran using machine learning algorithms.

It is necessary to predict hair mercury (Hg) levels and specify the related effective factors to develop preventive strategies to reduce Hg exposure in different regions. This study is the first effort to investigate the effectiveness of eight machine learning (ML) models (including multiple linear regression, decision tree regression, least absolute shrinkage and selection operator, multivariate adaptive regression splines, random forest, extreme gradient boosting, K-nearest neighbor, and Gaussian process) for predicting hair Hg levels and identifying the most important factors affecting them in residents of southwestern Iran. All ML models were trained with 70% of the dataset and their performance was evaluated using the determination coefficient (R), root mean square error (RMSE), and mean absolute error (MAE) based on the remaining dataset. Finally, the Permutation Feature Importance (PFI) method was used to determine the relative importance (RI) of influencing factors. Mean hair Hg (3.31 µg g⁻) was higher than the United States Environmental Protection Agency (US EPA) and World Health Organization (WHO) limits. It was indicated a high exposure risk for some people in this region. The extreme gradient boosting (XGB) model outperformed other algorithms in modeling hair Hg levels, with R = 0.61, RMSE = 2.2, and MAE = 1.25. According to the PFI analysis, weight (RI: 43.4%) and geographic place (RI: 41.8%) were found as the most important demographic factors influencing Hg variation in the study population. Additionally, occupation (RI: 46.1%) and the frequency of fish and canned fish consumption (RI: 22%) were identified as the most significant exposure factors controlling hair Hg variability in southwestern Iran. These findings can be useful for formulating appropriate strategies to reduce the health risk of Hg exposure and improve human health.