Enhanced prediction of spine surgery outcomes using advanced machine learning techniques and oversampling methods
The study highlights the potential of advanced machine learning techniques and oversampling methods in predicting spine surgery outcomes. The results underscore the importance of careful variable selection and model optimization to achieve optimal performance. This system holds promise as a tool to assist healthcare professionals in decision-making, thereby enhancing spine surgery outcomes. Future research should focus on further refining these models and exploring their application across larger datasets and diverse clinical settings.
Authors
- José Alberto Benítez‐Andrades (ORCID: https://orcid.org/0000-0002-4450-349X)
- Antonio Serrano García (ORCID: https://orcid.org/0000-0003-0704-3671)
- Camino Prada‐García (ORCID: https://orcid.org/0000-0002-2487-9350)
- Marta Esteban Blanco (ORCID: https://orcid.org/0000-0002-4013-0737)
- Nicolás Ordás-Reyes
- Alicia Merayo
Institutions
- Universidad de Valladolid (ES)
- Hospital Universitario de León (ES)
- Complejo Asistencial Universitario de Palencia (ES)
- Universidad de León (ES)
Publication Details
- Journal
- Health Information Science and Systems
- Published
- 2025-03-04
- DOI
- https://doi.org/10.1007/s13755-025-00343-9
- Citations
- 5
- Primary Topic
- Medical Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 3.37
Funders
- Universidad de León