An explainable AI framework for spatiotemporal risk factor analysis in public health: a case study of cardiovascular mortality in South Korea
Understanding environmental disease risk factor analysis at the district level is essential for gaining valuable insights into regional disease variations, offering a broader perspective compared to individual-level studies. Recently, explainable artificial intelligence (XAI) has received increasing...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-12-01
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| Series: | GIScience & Remote Sensing |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/15481603.2024.2436997 |
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