Machine learning approach to predict 1-year mortality after heart transplantation: a single-centre study
The findings indicate that ML models, particularly SVM and LR, are effective in predicting 1-year mortality post-heart transplantation as well as identifying significant predictors of mortality. This research contributes to the global knowledge in heart transplant and highlights the importance of new technologies in tailoring healthcare strategies for the Saudi population.
Authors
- Feras Khaliel
- Domenico Galzerano (ORCID: https://orcid.org/0000-0002-9682-3815)
- Adel Khwaji
- Valeria Pergola (ORCID: https://orcid.org/0000-0002-3317-7342)
- Hani Al Sergani
- Maria Teresa Savo (ORCID: https://orcid.org/0000-0003-1877-8550)
- Bandar Alamro (ORCID: https://orcid.org/0000-0002-8767-0108)
- Dan Alexandru Cozac (ORCID: https://orcid.org/0000-0002-9390-7263)
- Naji Kholaif (ORCID: https://orcid.org/0009-0003-6914-0730)
- Jehad Al Buraiki
- Giovanni Di Salvo (ORCID: https://orcid.org/0000-0002-0305-0113)
- Batol Allehyani
Institutions
- University of Technology Sydney (AU)
- Alfaisal University (SA)
- University of Padua (IT)
- King Faisal Specialist Hospital & Research Centre (SA)
- Universitatea de Medicină, Farmacie, Științe și Tehnologie „George Emil Palade” din Târgu Mureș (RO)
Publication Details
- Journal
- European Heart Journal - Imaging Methods and Practice
- Published
- 2025-10-01
- DOI
- https://doi.org/10.1093/ehjimp/qyaf100
- Citations
- 1
- Primary Topic
- Transplantation: Methods and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 2.01