ArticleBMC public health2025
Comparison of spatial prediction models from Machine Learning of cholangiocarcinoma incidence in Thailand.
Article in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundCholangiocarcinoma (CCA) poses a significant public health challenge in Thailand, with notably high incidence rates. This study aimed to compare the performance of spatial prediction models using Machine Learning techniques to analyze the occurrence of CCA across Thailand.
methodsThis retrospective cohort study analyzed CCA cases from four population-based cancer registries in Thailand, diagnosed between January 1, 2012, and December 31, 2021. The study employed Machine Learning models (Linear Regression, Random Forest, Neural Network, and Extreme Gradient Boosting (XGBoost)) to predict Age-Standardized Rates (ASR) of CCA based on spatial variables. Model performance was evaluated using Root Mean Square Error (RMSE) and R
resultsThe study included 6,379 CCA cases, with a male predominance (4,075 cases; 63.9%) and a mean age of 66.2 years (standard deviation = 11.1 years). The northeastern region accounted for most of the cases (3,898 cases; 61.1%). The overall ASR of CCA was 8.9 per 100,000 person-years (95% CI: 8.7 to 9.2), with the northeastern region showing the highest incidence (ASR = 13.4 per 100,000 person-years; 95% CI: 12.9 to 13.8). In the overall dataset, the Random Forest model demonstrated better prediction performance in both the training (R
conclusionThe 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.
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