Evaluating and Comparing Machine Learning Models for PM2.5 Health Impact Assessment in Thailand
Issued Date
2025-01-01
Resource Type
Scopus ID
2-s2.0-105040616334
Journal Title
6th Technology Innovation Management and Engineering Science International Conference Times Icon 2025 Proceedings
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SCOPUS
Bibliographic Citation
6th Technology Innovation Management and Engineering Science International Conference Times Icon 2025 Proceedings (2025)
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Nateeprasittipon P., Sa-Nga-Ngam P., Chansutthirangkool M. Evaluating and Comparing Machine Learning Models for PM2.5 Health Impact Assessment in Thailand. 6th Technology Innovation Management and Engineering Science International Conference Times Icon 2025 Proceedings (2025). doi:10.1109/TIMES-iCON67125.2025.11488165 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/117182
Title
Evaluating and Comparing Machine Learning Models for PM2.5 Health Impact Assessment in Thailand
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Abstract
This study presents a methodological comparison of four machine learning and statistical models to assess the acute impact of fine particulate matter (PM2.5) on public health in Northern Thailand. The performance of Random Forest (RF) and Long Short-Term Memory (LSTM) was evaluated against two baselines: Linear Regression (LR) and SARIMAX. Air quality data and health surveillance data from 2023 were integrated for four disease groups in Health Region 2. The final merged datasets comprised approximately 5 0 0 - 6 0 0 provinceday records per disease group. The results demonstrate that Random Forest was the most robust model, achieving the highest R -squared scores for Skin (R<sup>2</sup>=0.21) and Eye diseases (R<sup>2</sup> = 0. 1 8). The LSTM model showed competitive performance, ranking second, whereas Linear Regression failed to capture the non-linear patterns. Conversely, all models yielded negative R<sup>2</sup> values for Cardiovascular disease, suggesting that short-term exposure (2-day lag) is insufficient for predicting heart-related conditions.