Predicting cardiovascular outcomes in Chinese patients with type 2 diabetes by combining risk factor trajectories and machine learning algorithm: a cohort study
The ML-CVD-C model, incorporating dynamic cardiovascular risk trajectories and a machine learning algorithm, significantly improves risk prediction accuracy for Chinese patients with diabetes. This model may serve as a valuable tool for more personalized cardiovascular risk management in type 2 diabetes.
Authors
- Shuohua Chen (ORCID: https://orcid.org/0000-0002-6274-7471)
- Zhouhui Lian (ORCID: https://orcid.org/0000-0002-2683-7170)
- Yingying Luo (ORCID: https://orcid.org/0000-0002-6391-4489)
- Xianghai Zhou (ORCID: https://orcid.org/0000-0003-2340-6727)
- Yanxiu Wang (ORCID: https://orcid.org/0000-0002-1220-8672)
- Xiantong Zou (ORCID: https://orcid.org/0000-0002-2349-3042)
- Qi Huang (ORCID: https://orcid.org/0000-0002-9761-1136)
- Linong Ji (ORCID: https://orcid.org/0000-0002-5121-8371)
- Xueyao Han (ORCID: https://orcid.org/0000-0002-2387-4937)
- Shouling Wu
Institutions
- Peking University (CN)
- Kailuan General Hospital (CN)
- Peking University People's Hospital (CN)
Publication Details
- Journal
- Cardiovascular Diabetology
- Published
- 2025-02-07
- DOI
- https://doi.org/10.1186/s12933-025-02611-0
- Citations
- 9
- Primary Topic
- Cardiovascular Health and Risk Factors
- Type
- article
- Field-Weighted Citation Impact
- 12.49
Funders
- National Natural Science Foundation of China
- National Science and Technology Major Project