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Predicting major adverse cardiac events in diabetes and chronic kidney disease: a machine learning study from the Silesia Diabetes-Heart Project
18
Zitationen
15
Autoren
2025
Jahr
Abstract
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.
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Autoren
Institutionen
- Medical University of Silesia(PL)
- University of Liverpool(GB)
- Liverpool Heart and Chest Hospital(GB)
- The Affiliated Yongchuan Hospital of Chongqing Medical University(CN)
- Liverpool John Moores University(GB)
- Chongqing Medical University(CN)
- Nanchang University(CN)
- Second Affiliated Hospital of Nanchang University(CN)
- University of Modena and Reggio Emilia(IT)
- Azienda Ospedaliero-Universitaria di Modena(IT)
- Aintree University Hospitals NHS Foundation Trust(GB)
- Aalborg University(DK)