Prediction model for cardiovascular disease in patients with diabetes using machine learning derived and validated in two independent Korean cohorts
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Sang, Hyunji | - |
dc.contributor.author | Lee, Hojae | - |
dc.contributor.author | Lee, Myeongcheol | - |
dc.contributor.author | Park, Jaeyu | - |
dc.contributor.author | Kim, Sunyoung | - |
dc.contributor.author | Woo, Ho Geol | - |
dc.contributor.author | Rahmati, Masoud | - |
dc.contributor.author | Koyanagi, Ai | - |
dc.contributor.author | Smith, Lee | - |
dc.contributor.author | Lee, Sihoon | - |
dc.contributor.author | Hwang, You-Cheol | - |
dc.contributor.author | Park, Tae Sun | - |
dc.contributor.author | Lim, Hyunjung | - |
dc.contributor.author | Yon, Dong Keon | - |
dc.contributor.author | Rhee, Sang Youl | - |
dc.date.accessioned | 2024-07-27T11:00:27Z | - |
dc.date.available | 2024-07-27T11:00:27Z | - |
dc.date.issued | 2024-06 | - |
dc.identifier.issn | 2045-2322 | - |
dc.identifier.issn | 2045-2322 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/92082 | - |
dc.description.abstract | This study aimed to develop and validate a machine learning (ML) model tailored to the Korean population with type 2 diabetes mellitus (T2DM) to provide a superior method for predicting the development of cardiovascular disease (CVD), a major chronic complication in these patients. We used data from two cohorts, namely the discovery (one hospital; n = 12,809) and validation (two hospitals; n = 2019) cohorts, recruited between 2008 and 2022. The outcome of interest was the presence or absence of CVD at 3 years. We selected various ML-based models with hyperparameter tuning in the discovery cohort and performed area under the receiver operating characteristic curve (AUROC) analysis in the validation cohort. CVD was observed in 1238 (10.2%) patients in the discovery cohort. The random forest (RF) model exhibited the best overall performance among the models, with an AUROC of 0.830 (95% confidence interval [CI] 0.818-0.842) in the discovery dataset and 0.722 (95% CI 0.660-0.783) in the validation dataset. Creatinine and glycated hemoglobin levels were the most influential factors in the RF model. This study introduces a pioneering ML-based model for predicting CVD in Korean patients with T2DM, outperforming existing prediction tools and providing a groundbreaking approach for early personalized preventive medicine. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | NATURE PORTFOLIO | - |
dc.title | Prediction model for cardiovascular disease in patients with diabetes using machine learning derived and validated in two independent Korean cohorts | - |
dc.type | Article | - |
dc.identifier.wosid | 001258865400023 | - |
dc.identifier.doi | 10.1038/s41598-024-63798-y | - |
dc.identifier.bibliographicCitation | SCIENTIFIC REPORTS, v.14, no.1 | - |
dc.description.isOpenAccess | Y | - |
dc.identifier.scopusid | 2-s2.0-85197169059 | - |
dc.citation.title | SCIENTIFIC REPORTS | - |
dc.citation.volume | 14 | - |
dc.citation.number | 1 | - |
dc.type.docType | Article | - |
dc.publisher.location | 독일 | - |
dc.subject.keywordAuthor | Machine learning | - |
dc.subject.keywordAuthor | Cardiovascular diseases | - |
dc.subject.keywordAuthor | Diabetes mellitus | - |
dc.subject.keywordAuthor | Prediction | - |
dc.subject.keywordAuthor | Random forest model | - |
dc.subject.keywordPlus | HEART-FAILURE | - |
dc.subject.keywordPlus | RISK-FACTORS | - |
dc.subject.keywordPlus | LIFE-STYLE | - |
dc.subject.keywordPlus | FOLLOW-UP | - |
dc.subject.keywordPlus | COMPLICATIONS | - |
dc.subject.keywordPlus | VARIABILITY | - |
dc.subject.keywordPlus | PREVENTION | - |
dc.relation.journalResearchArea | Science & Technology - Other Topics | - |
dc.relation.journalWebOfScienceCategory | Multidisciplinary Sciences | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
1342, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Republic of Korea(13120)031-750-5114
COPYRIGHT 2020 Gachon University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.