Comparison of spatial prediction models from Machine Learning of cholangiocarcinoma incidence in Thailand
The Random Forest model demonstrated the highest efficiency in predicting CCA incidence rates in Thailand, though predictive performance varied across regions. Spatial factors effectively predicted ASR of CCA, providing valuable insights for national-level disease surveillance and targeted public health interventions. These findings support the development of region-specific approaches for CCA control using spatial epidemiology and machine learning techniques.
Authors
- Matías García-Constantino (ORCID: https://orcid.org/0000-0002-3420-0532)
- Apiradee Lim (ORCID: https://orcid.org/0000-0002-7137-4101)
- Supot Kamsa‐ard (ORCID: https://orcid.org/0000-0002-1554-7696)
- Idongesit Ekerete (ORCID: https://orcid.org/0000-0003-4072-7178)
- Siriporn Kamsa‐ard
- Oraya Sahat
Institutions
- Prince of Songkla University (TH)
- University of Ulster (GB)
- Khon Kaen University (TH)
Publication Details
- Journal
- BMC Public Health
- Published
- 2025-06-07
- DOI
- https://doi.org/10.1186/s12889-025-23119-y
- Primary Topic
- Cholangiocarcinoma and Gallbladder Cancer Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Ulster University
- Khon Kaen University