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Prediction of cardiovascular outcomes with machine learning techniques: application to the Cardiovascular Outcomes in Renal Atherosclerotic Lesions (CORAL) study

Abstract:
Background Data derived from the Cardiovascular Outcomes in Renal Atherosclerotic Lesions (CORAL) study were analyzed in an effort to employ machine learning methods to predict the composite endpoint described in the original study. Methods We identified 573 CORAL subjects with complete baseline data and the presence or absence of a composite endpoint for the study. These data were subjected to several models including a generalized linear (logistic-linear) model, support vector machine, decision tree, feed-forward neural network, and random forest, in an effort to attempt to predict the composite endpoint. The subjects were arbitrarily divided into training and testing subsets according to an 80%:20% distribution with various seeds. Prediction models were optimized within the CARET package of R. Results The best performance of the different machine learning techniques was that of the random forest method which yielded a receiver operator curve (ROC) area of 68.1%±4.2% (mean ± SD) on the testing subset with ten different seed values used to separate training and testing subsets. The four most important variables in the random forest method were SBP, serum creatinine, glycosylated hemoglobin, and DBP. Each of these variables was also important in at least some of the other methods. The treatment assignment group was not consistently an important determinant in any of the models. Conclusion Prediction of a composite cardiovascular outcome was difficult in the CORAL population, even when employing machine learning methods. Assignment to either the stenting or best medical therapy group did not serve as an important predictor of composite outcome. Clinical Trial Registration ClinicalTrials.gov, NCT00081731
Author Listing: Tian Chen;Pamela Brewster;Katherine R Tuttle;Lance D Dworkin;William Henrich;Barbara A Greco;Michael Steffes;Sheldon Tobe;Kenneth Jamerson;Karol Pencina;Joseph M Massaro;Ralph B D’Agostino;Donald E Cutlip;Timothy P Murphy;Christopher J Cooper;Joseph I Shapiro
Volume: 12
Pages: 49 - 58
DOI: 10.2147/IJNRD.S194727
Language: English
Journal: International Journal of Nephrology and Renovascular Disease

International Journal of Nephrology and Renovascular Disease

INT J NEPHROL RENOV

影响因子:2.5
是否综述期刊:否
是否OA:是
是否预警:不在预警名单内
发行时间:-
ISSN:1178-7058
发刊频率:-
收录数据库:ESCI/Scopus收录/DOAJ开放期刊
出版国家/地区:United Kingdom
出版社:Dove Medical Press

期刊介绍

年发文量 31
国人发稿量 2
国人发文占比 8%
自引率 0.0%
平均录取率 -
平均审稿周期 16 Weeks
版面费 US$2320
偏重研究方向 UROLOGY & NEPHROLOGY-
期刊官网 http://www.dovepress.com/international-journal-of-nephrology-and-renovascular-disease-journal
投稿链接 -

质量指标占比

研究类文章占比 OA被引用占比 撤稿占比 出版后修正文章占比
83.87% 100.00% - -

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预警情况

时间 预警情况
2025年03月发布的2025版 不在预警名单中
2024年02月发布的2024版 不在预警名单中
2023年01月发布的2023版 不在预警名单中
2021年12月发布的2021版 不在预警名单中
2020年12月发布的2020版 不在预警名单中

JCR分区 WOS分区等级:Q2区

版本 按学科 分区
WOS期刊SCI分区
(2021-2022年最新版)
UROLOGY & NEPHROLOGY Q3

中科院分区

版本 大类学科 小类学科 Top期刊 综述期刊
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