FMLCA: explainable and privacy-preserving federated machine learning classification algorithms for predicting heart disease in patients
Implementing these models on a cloud platform results in efficient computational performance. This proposed approach represents a significant advancement in predictive healthcare tools, capable of accurately predicting CAD across distributed environments. By placing a strong emphasis on privacy and security, this approach underscores its importance and paves the way for a transformative healthcare ecosystem that centers on the needs of patients and healthcare providers.
Authors
- Amir Sorayaie Azar (ORCID: https://orcid.org/0000-0001-7797-7790)
- Jamshid Bagherzadeh (ORCID: https://orcid.org/0000-0003-2497-0186)
- Asghar Asl Asgharian Sardroud
- Uffe Kock Wiil
- Fardin Gholami
- Leila Sharifi
Institutions
- University of Southern Denmark (DK)
- Maersk (Denmark) (DK)
- Urmia University (IR)
Publication Details
- Journal
- European journal of medical research
- Published
- 2026-02-12
- DOI
- https://doi.org/10.1186/s40001-026-04023-6
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00