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February 16, 2026European journal of medical research2 citationsOpen Access

FMLCA: explainable and privacy-preserving federated machine learning classification algorithms for predicting heart disease in patients

AAAmir Sorayaie AzarFGFardin GholamiLSLeila Sharifi

Key Points

  • The central aim is to develop federated machine learning algorithms that prioritize privacy while predicting heart disease.
  • Implemented algorithms on a cloud platform for computational efficiency.
  • Focused on explainability and privacy in model design.
  • Utilized federated learning across distributed environments.
  • Achieved accurate predictions of coronary artery disease (CAD).
  • Demonstrated improved computational performance in a cloud-based infrastructure.

Abstract

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.

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Cite This Study

Azar et al. (2026) studied this question.

synapsesocial.com/papers/6992b3319b75e639e9b080bbhttps://doi.org/10.1186/s40001-026-04023-6
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