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\n[https://doi.org/10.1186/s12911-024-02590-4](https://doi.org/10.1186/s12911-024-02590-4)  \nBMC Medical Informatics and Decision Making  \nRESEARCH Open Access  \nEstablishment of prediction model for mortality risk of pancreatic cancer: a retrospective study  \nRaoof Nopour 1*  \nAbstract  \nBackground and aim Pancreatic cancer possesses a high prevalence and mortality rate among other cancers. Despite the low survival rate of this cancer type, the early prediction of this disease has a crucial role in decreasing the mortality rate and improving the prognosis. So, this study.  \nMaterials and methods In this retrospective study, we used 654 alive and dead PC cases to establish the prediction model for PC. The six chosen machine learning algorithms and prognostic factors were utilized to build the prediction models. The importance of the predictive factors was assessed using the relative importance of a high-performing algorithm.  \nResults The XG-Boost with AU-ROC of 0.933 (95% CI= [0 .906–0. 958]) and AU-ROC of 0.836 (95% CI= [0 .789–0. 865] in internal and external validation modes were considered as the best-performing model for predicting the mortality risk of PC. The factors, including tumor size, smoking, and chemotherapy, were considered the most influential for prediction.  \nConclusion The XG-Boost gained more performance efficiency in predicting the mortality risk of PC patients, so this model can promote the clinical solutions that doctors can achieve in healthcare environments to decrease the mortality risk of these patients.  \nHighlights  \n• We developed machine learning models to predict the mortality risk of pancreatic cancer.  \n• XG-Boost demonstrated more competency in predicting mortality risk.  \n• Prognostic factors are essential for predicting the mortality risk of PC.  \n• Based on the external validation results, the clinical applicability of the XG-Boost is almost efficient in other clinical environments.  \n• Some lifestyle factors, such as smoking, have a significant role in predicting the mortality risk on this topic. Keywords Pancreatic cancer, Mortality risk, Machine learning, Prediction model, Prognostic factors  \n*Correspondence:  \nRaoof Nopour  \n[raoof.n1370@gmail.com](raoof.n1370@gmail.com)  \n1Department of Health Information Management, Student Research Committee, School of Health Management and Information Sciences Branch, Iran University of Medical Sciences, Tehran, Iran  \n© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creativecommons.org/l](http://creativecommons.org/l)icenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ([http://creativecommons.org/publicdomain/zero/1.0/](http://creativecommons.org/publicdomain/zero/1.0/)) applies to the data made available in this article, unless otherwise stated in a credit line to the data.  \nNopour BMC Medical Informatics and Decision Making (2024) 24:181  \nIntroduction  \nPancreatic cancer (PC) refers to the uncontrollable growth of cells in the pancreas gland, causing a cancerous mass that can spread to other tissues in the body [1]. More than 90% of this cancer type is formed from ductal epithelium, namel","cbCair4VSD4Z1HHW","https://ap.wps.com/l/cbCair4VSD4Z1HHW","pdf",1471362,11,"English","# Abstract\n## Background and aim\n## Materials and methods\n## Results\n## Conclusion\n# Introduction\n## Disease burden and epidemiology\n## Need for predictive tools","[{\"question\":\"What is the study’s main goal?\",\"answer\":\"To establish a prediction model for mortality risk in pancreatic cancer and improve early risk assessment to support better prognosis.\"},{\"question\":\"How was the prediction model developed?\",\"answer\":\"Using a retrospective dataset of 654 alive and dead pancreatic cancer cases, the study applied six machine learning algorithms with prognostic factors to build the models.\"},{\"question\":\"Which algorithm performed best?\",\"answer\":\"XG-Boost showed the best performance for predicting pancreatic cancer mortality risk in both internal and external validation, with the highest AU-ROC values reported.\"}]","Establishment of prediction model for mortality risk of pancreatic cancer: a retrospective study - Research | PDF",1789997197,28]