Enhancing Cardiovascular Risk Prediction: Development of an Advanced Xgboost Model with Hospital-Level Random Effects
These findings suggest that integrating mixed effects into machine learning models can enhance their performance on datasets where the sample size is small.
Authors
- Alberto Freitas (ORCID: https://orcid.org/0000-0003-2113-9653)
- Tim Dong (ORCID: https://orcid.org/0000-0003-1953-0063)
- Massimo Caputo (ORCID: https://orcid.org/0000-0001-7508-0891)
- Iyabosola Busola Oronti (ORCID: https://orcid.org/0000-0001-5324-6408)
- Bing Zhai
- Gianni D. Angelini (ORCID: https://orcid.org/0000-0002-1753-3730)
- Jeremy Chan (ORCID: https://orcid.org/0000-0002-3009-5712)
- Shubhra Sinha (ORCID: https://orcid.org/0000-0001-8554-6704)
- Daniel Fudulu (ORCID: https://orcid.org/0000-0001-6759-7719)
Institutions
- Universidade do Porto (PT)
- Northumbria University (GB)
- University of Bristol (GB)
- University of Warwick (GB)
Publication Details
- Journal
- Bioengineering
- Published
- 2024-10-18
- DOI
- https://doi.org/10.3390/bioengineering11101039
- Citations
- 6
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 2.08
Funders
- Health and Care Research Wales