Prediction model for cardiovascular disease in patients with diabetes using machine learning derived and validated in two independent Korean cohorts.
Sci Rep
; 14(1): 14966, 2024 06 28.
Article
in En
| MEDLINE
| ID: mdl-38942775
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.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Cardiovascular Diseases
/
Diabetes Mellitus, Type 2
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Machine Learning
Limits:
Aged
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Female
/
Humans
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Male
/
Middle aged
Country/Region as subject:
Asia
Language:
En
Journal:
Sci Rep
Year:
2024
Document type:
Article
Affiliation country: