Transfer learning for a tabular-to-image approach: A case study for cardiovascular disease prediction
To the best of our knowledge, this is the first study that evaluates the effectiveness of applying transfer learning and fine-tuning to tabular data using tabular-to-image approaches. Through the use of CNNs' predictive capabilities, our work also advances the diagnosis of CVD by providing a framework for early clinical intervention and decision-making support.
Authors
- David Chushig-Muzo (ORCID: https://orcid.org/0000-0001-5585-2305)
- Ana M. Wägner (ORCID: https://orcid.org/0000-0002-7663-9308)
- Conceição Granja (ORCID: https://orcid.org/0000-0002-3028-8899)
- Cristina Soguero-Ruíz (ORCID: https://orcid.org/0000-0001-5817-989X)
- Vanesa Gómez-Martínez (ORCID: https://orcid.org/0009-0001-3349-3900)
- Francisco J. Lara-Abelenda (ORCID: https://orcid.org/0000-0001-5565-8203)
- Pablo Peiro-Corbacho
Institutions
- Universidad de Las Palmas de Gran Canaria (ES)
- University Hospital of North Norway (NO)
- Universidad Rey Juan Carlos (ES)
Publication Details
- Journal
- Journal of Biomedical Informatics
- Published
- 2025-04-08
- DOI
- https://doi.org/10.1016/j.jbi.2025.104821
- Citations
- 9
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 22.33
Funders
- European Commission