Development and External Validation of a Machine Learning-Based Model for Predicting Heart Failure Risk in Type 2 Diabetes
We developed and validated an interpretable machine learning-based nomogram that effectively predicts the risk of elevated NT-proBNP in T2DM patients using six routine clinical variables. This tool demonstrates robust performance and generalizability, offering a practical and accessible solution for HF risk stratification in resource-limited primary care settings in China.
Authors
- Y Y Liu (ORCID: https://orcid.org/0009-0009-0532-7436)
- Sania M. Kasyanju (ORCID: https://orcid.org/0000-0001-7342-2077)
- Ping Wang (ORCID: https://orcid.org/0000-0001-8992-1233)
- Peng Li (ORCID: https://orcid.org/0000-0002-7751-1146)
- Min Wang (ORCID: https://orcid.org/0009-0003-5120-7386)
- Tao Yang (ORCID: https://orcid.org/0000-0001-6375-3622)
- Min Sun (ORCID: https://orcid.org/0000-0002-7049-5526)
- Yuhong Yang (ORCID: https://orcid.org/0000-0002-3639-9055)
- Yan Chen (ORCID: https://orcid.org/0000-0002-2540-0436)
Institutions
- Gaochun People's Hospital (CN)
- Second Affiliated Hospital of Nanjing Medical University (CN)
- Nanjing Medical University (CN)
Publication Details
- Journal
- Diabetes Metabolic Syndrome and Obesity
- Published
- 2025-11-01
- DOI
- https://doi.org/10.2147/dmso.s558687
- Primary Topic
- Heart Failure Treatment and Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Government of Jiangsu Province