Prediction model for cardiovascular disease in patients with diabetes using machine learning derived and validated in two independent Korean cohortsopen access
- Authors
- Sang, Hyunji; Lee, Hojae; Lee, Myeongcheol; Park, Jaeyu; Kim, Sunyoung; Woo, Ho Geol; Rahmati, Masoud; Koyanagi, Ai; Smith, Lee; Lee, Sihoon; Hwang, You-Cheol; Park, Tae Sun; Lim, Hyunjung; Yon, Dong Keon; Rhee, Sang Youl
- Issue Date
- Jun-2024
- Publisher
- NATURE PORTFOLIO
- Keywords
- Machine learning; Cardiovascular diseases; Diabetes mellitus; Prediction; Random forest model
- Citation
- SCIENTIFIC REPORTS, v.14, no.1
- Journal Title
- SCIENTIFIC REPORTS
- Volume
- 14
- Number
- 1
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/92082
- DOI
- 10.1038/s41598-024-63798-y
- ISSN
- 2045-2322
2045-2322
- 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.
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