A Machine Learning Perspective on the Climatic and Socioeconomic Determinants of Mental Health in Southeast Asia
The growing burden of mental health disorders necessitates a comprehensive understanding of their environmental and socioeconomic determinants. This study employs machine learning to analyze the relationship between mental health mortality and key socioeconomic and climatic factors across Southeast...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
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| Series: | World |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4060/6/2/48 |
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| Summary: | The growing burden of mental health disorders necessitates a comprehensive understanding of their environmental and socioeconomic determinants. This study employs machine learning to analyze the relationship between mental health mortality and key socioeconomic and climatic factors across Southeast Asia. Using a Random Forest model (R<sup>2</sup> = 0.95), we identify the population size and the Physical Quality of Life Index (PQLI) as the strongest predictors of mental health mortality, while climate indices—the proportion of warm nights (TN90p) and hot days (TX90p)—exhibit weaker direct effects (importance < 0.1), but significant indirect effects through socioeconomic pathways. The regional disparities highlight Indonesia as the most climate-sensitive country, whereas the Philippines shows weaker climate–mortality correlations, suggesting that its socioeconomic resilience and healthcare infrastructure can mitigate climate impacts. These findings underscore the need for integrated climate–mental health strategies, particularly for vulnerable regions experiencing extreme temperatures and socioeconomic stressors. |
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| ISSN: | 2673-4060 |