Predicting major adverse cardiac events in diabetes and chronic kidney disease: a machine learning study from the Silesia Diabetes-Heart Project
Ten features-based ML models, especially the LGBM model, had acceptable performance in predicting CVEs in persons with DM and CKD. A decrease in eGFR, aging, and elevated inflammatory markers significantly enhanced the predictive capability of the model. Future external validation of our model is required prior to implementation in a clinical environment.
Authors
- Oliwia Janota (ORCID: https://orcid.org/0000-0002-0597-4846)
- Uazman Alam (ORCID: https://orcid.org/0000-0002-3190-1122)
- Wiktoria Wójcik
- Marta Mantovani (ORCID: https://orcid.org/0009-0006-7290-5361)
- Krzysztof Irlik (ORCID: https://orcid.org/0000-0001-9699-1686)
- Julia Piaśnik (ORCID: https://orcid.org/0009-0004-4813-6123)
- Katarzyna Nabrdalik (ORCID: https://orcid.org/0000-0002-0777-8048)
- Janusz Gumprecht (ORCID: https://orcid.org/0000-0001-7091-2118)
- Mirela Hendel
- Yalin Zheng (ORCID: https://orcid.org/0000-0002-7873-0922)
- Yang Chen
- Hanna Kwiendacz (ORCID: https://orcid.org/0000-0002-1256-0414)
- Yang Liu (ORCID: https://orcid.org/0000-0003-4430-3452)
- Bi Huang
- Gregory Y. H. Lip
Institutions
- University of Modena and Reggio Emilia (IT)
- Nanchang University (CN)
- University of Liverpool (GB)
- Medical University of Silesia (PL)
- Aintree University Hospitals NHS Foundation Trust (GB)
- Liverpool Heart and Chest Hospital (GB)
- Second Affiliated Hospital of Nanchang University (CN)
- Azienda Ospedaliero-Universitaria di Modena (IT)
- The Affiliated Yongchuan Hospital of Chongqing Medical University (CN)
- Liverpool John Moores University (GB)
- Chongqing Medical University (CN)
- Aalborg University (DK)
Publication Details
- Journal
- Cardiovascular Diabetology
- Published
- 2025-02-15
- DOI
- https://doi.org/10.1186/s12933-025-02615-w
- Citations
- 18
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 52.88
Funders
- Uniwersytet Śląski w Katowicach