Federated learning-based multimodal approach for early detection and personalized care in cardiac disease
Our framework enables early diagnosis and personalized lifestyle recommendations while maintaining strict data confidentiality. The combination of federated learning and multimodal feature fusion offers a scalable, privacy-centric solution for heart disease management, with strong potential for real-world clinical implementation.
Authors
- Seyed Jalaleddin Mousavirad (ORCID: https://orcid.org/0000-0001-8661-7578)
- Ghanshyam G. Tejani (ORCID: https://orcid.org/0000-0001-9106-0313)
- Sunil Kumar Sharma (ORCID: https://orcid.org/0000-0002-6809-2410)
- Sultan Alasmari (ORCID: https://orcid.org/0000-0003-2485-0686)
- Rayed AlGhamdi (ORCID: https://orcid.org/0000-0002-6277-2124)
Institutions
- King Abdulaziz University (SA)
- Majmaah University (SA)
- Riyadh Elm University (SA)
- Mid Sweden University (SE)
- Saveetha University (IN)
- Yuan Ze University (TW)
Publication Details
- Journal
- Frontiers in Physiology
- Published
- 2025-04-23
- DOI
- https://doi.org/10.3389/fphys.2025.1563185
- Citations
- 7
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 20.56