{"title":"Establishment of prediction model for mortality risk of pancreatic cancer: a retrospective study.","authors":"Raoof Nopour","doi":"10.1186/s12911-024-02590-4","DOIUrl":null,"url":null,"abstract":"<p><strong>Background and aim: </strong>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.</p><p><strong>Materials and methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":null,"pages":null},"PeriodicalIF":3.3000,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11210158/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC Medical Informatics and Decision Making","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s12911-024-02590-4","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MEDICAL INFORMATICS","Score":null,"Total":0}
引用次数: 0
Abstract
Background 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.
Materials 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.
Results: 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.
Conclusion: 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.
背景和目的:在其他癌症中,胰腺癌的发病率和死亡率都很高。尽管这种癌症的存活率很低,但早期预测这种疾病对于降低死亡率和改善预后有着至关重要的作用。因此,我们开展了这项研究:在这项回顾性研究中,我们利用 654 例存活和死亡的 PC 病例建立了 PC 预测模型。我们选择了六种机器学习算法和预后因素来建立预测模型。预测因素的重要性采用高性能算法的相对重要性进行评估:XG-Boost的AU-ROC为0.933(95% CI=[0.906-0.958]),内部和外部验证模式的AU-ROC为0.836(95% CI=[0.789-0.865]),被认为是预测PC死亡风险的最佳模型。包括肿瘤大小、吸烟和化疗在内的因素被认为对预测影响最大:XG-Boost在预测PC患者死亡风险方面获得了更高的性能效率,因此该模型可以促进医生在医疗环境中实现降低PC患者死亡风险的临床解决方案。
期刊介绍:
BMC Medical Informatics and Decision Making is an open access journal publishing original peer-reviewed research articles in relation to the design, development, implementation, use, and evaluation of health information technologies and decision-making for human health.