[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122304-en":3,"doc-seo-122304-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122304,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Comparison of spatial prediction models from Machine Learning of cholangiocarcinoma incidence in Thailand - research summary","Cholangiocarcinoma (CCA) presents a major public health burden in Thailand, with consistently high incidence. This retrospective cohort study compared spatial prediction performance of multiple machine learning models to estimate CCA occurrence across Thailand. Using 6,379 cases from four population-based cancer registries (2012–2021), models were trained on spatial variables and evaluated with RMSE and R2. Random Forest achieved the strongest overall predictive accuracy, while performance varied by region; key spatial predictors included elevation and distance from water sources.","Comparison of spatial prediction models from Machine Learning of  \ncholangiocarcinoma incidence in Thailand  \nSahat, O. , Kamsa-ard, S. , Lim, A. , Kamsa-ard, S. , Garcia-Constantino, M. , & Ekerete, I. (2025) . Comparison of spatial prediction models from Machine Learning of cholangiocarcinoma incidence in Thailand. BMC Public Health, 25(1), 1-12 . Article 2137. Advance online publication. [https://doi.org/10.1186/s12889-025-23119-y](https://doi.org/10.1186/s12889-025-23119-y)  \nLink to publication record in Ulster University Research Portal  \nPublished in:  \nBMC Public Health  \nPublication Status:  \nPublished online: 07/06/2025  \nDOI:  \n10.1186/s12889-025-23119-y  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nDocument Licence:  \nCC BY-NC-ND  \nGeneral rights  \nThe copyright and moral rights to the output are retained by the output author(s), unless otherwise stated by the document licence.  \nUnless otherwise stated, users are permitted to download a copy of the output for personal study or non-commercial research and are permitted to freely distribute the URL of the output. They are not permitted to alter, reproduce, distribute or make any commercial use of the output without obtaining the permission of the author(s) .  \nIf the document is licenced under Creative Commons, the rights of users of the documents can be found at [https://creativecommons.org/share-your-work/cclicenses/](https://creativecommons.org/share-your-work/cclicenses/) .  \nTake down policy  \nThe Research Portal is Ulster University's institutional repository that provides access to Ulster's research outputs. Every effort has been made to ensure that content in the Research Portal does not infringe any person's rights, or applicable UK laws. If you discover content in the Research Portal that you believe breaches copyright or violates any law, please contact [pure-support@ulster.ac.uk](pure-support@ulster.ac.uk)  \nDownload date: 01/07/2025  \nSahat etal. BMC Public Health (2025) 25:2137 [https://doi.org/10.1186/s12889-025-23119-y](https://doi.org/10.1186/s12889-025-23119-y)  \nBMC Public Health  \n RESEARCH Open Access  \nComparison of spatial prediction models  \nfrom Machine Learning of cholangiocarcinoma incidence in Thailand  \nOraya Sahat1, Supot Kamsa‑ard2*, Apiradee Lim3, Siriporn Kamsa‑ard2, Matias Garcia‑Constantino4 and Idongesit Ekerete4  \nAbstract  \nBackground Cholangiocarcinoma (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.  \nMethods This retrospective cohort study analyzed CCA cases from four population‑based cancer registries in Thai‑ land, 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‑Stand‑ ardized Rates (ASR) of CCA based on spatial variables. Model performance was evaluated using Root Mean Square Error (RMSE) and R2 with 70:30 train‑test validation.  \nResults The 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 (R2 = 72 . 07%) and test‑ ing datasets (R2 = 71 . 66%) . Regional variations in model performance were observed, with Random Forest performing best in the northern, northeastern regions, while XGBoost excelled in the central and southern regions. The most important spatia","cbCailNAjbGX9C6T","https://ap.wps.com/l/cbCailNAjbGX9C6T","pdf",2290374,1,13,"English","en",105,"# Abstract\n## Background\n## Methods\n## Results\n## Conclusion\n# Keywords","[{\"question\":\"What was the purpose of this study on cholangiocarcinoma in Thailand?\",\"answer\":\"To compare the performance of spatial prediction models using machine learning for analyzing the occurrence of cholangiocarcinoma across Thailand.\"},{\"question\":\"Which machine learning models were used to predict CCA incidence?\",\"answer\":\"Linear Regression, Random Forest, Neural Network, and XGBoost were used to predict age-standardized rates (ASR) based on spatial variables.\"},{\"question\":\"What model performed best overall, and which spatial predictors were most important?\",\"answer\":\"Random Forest showed the best overall predictive performance, and the most important spatial predictors were elevation and distance from water sources.\"}]","Comparison of spatial prediction models from Machine Learning of cholangiocarcinoma incidence in Thailand - research summary | PDF",1785809909,33,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"comparison-of-spatial-prediction-models-from-machine-learning-of-cholangiocarcinoma-incidence-in-thailand-research-summary","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/comparison-of-spatial-prediction-models-from-machine-learning-of-cholangiocarcinoma-incidence-in-thailand-research-summary/122304/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What was the purpose of this study on cholangiocarcinoma in Thailand?","Question",{"text":75,"@type":76},"To compare the performance of spatial prediction models using machine learning for analyzing the occurrence of cholangiocarcinoma across Thailand.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models were used to predict CCA incidence?",{"text":80,"@type":76},"Linear Regression, Random Forest, Neural Network, and XGBoost were used to predict age-standardized rates (ASR) based on spatial variables.",{"name":82,"@type":73,"acceptedAnswer":83},"What model performed best overall, and which spatial predictors were most important?",{"text":84,"@type":76},"Random Forest showed the best overall predictive performance, and the most important spatial predictors were elevation and distance from water sources.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]