ArticleDiagnostics (Basel, Switzerland)2023
FedEHR: A Federated Learning Approach towards the Prediction of Heart Diseases in IoT-Based Electronic Health Records.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Quantum-enhanced federated blockchain for privacy-preserving cardiovascular intelligence.Scientific reports · 2026Article
- Federated clinical concept and disease semantic learning for congenital heart disease diagnosis.NPJ digital medicine · 2026Article
- Development and validation of random-forest based federated ensemble learning algorithms for delirium prediction using electronic medical records from eleven hospitals in Austria: a retrospective study.BMC medical informatics and decision making · 2026Article
- Federated learning for cardiovascular disease prediction: a systematic review of clinical applications, validation, and translation readiness.Frontiers in cardiovascular medicine · 2026Review
- Federated Learning for Cardiovascular Disease Prediction: A Comparative Review of Biosignal- and EHR-Based Approaches.Healthcare (Basel, Switzerland) · 2025Review
- Federated Learning in Public Health: A Systematic Review of Decentralized, Equitable, and Secure Disease Prevention Approaches.Healthcare (Basel, Switzerland) · 2025Review
- A deep learning approach for heart disease detection using a modified multiclass attention mechanism with BiLSTM.Scientific reports · 2025Article
- IoT driven smart health monitoring for heart disease prediction using quantum kernel enhanced sardine diffusion and CNN.Scientific reports · 2025Article
- Federated learning with multi-cohort real-world data for predicting the progression from mild cognitive impairment to Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Federated learning of medical concepts embedding using BEHRT.JAMIA open · 2024Article
- FedHealthFog: A federated learning-enabled approach towards healthcare analytics over fog computing platform.Heliyon · 2024Article
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Authors and funding
4 authors.
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Abstract
In contemporary healthcare, the prediction and identification of cardiac diseases is crucial. By leveraging the capabilities of Internet of Things (IoT)-enabled devices and Electronic Health Records (EHRs), the healthcare sector can largely benefit to improve patient outcomes by increasing the accuracy of disease prediction. However, protecting data privacy is essential to promote participation and adhere to rules. The suggested methodology combines EHRs with IoT-generated health data to predict heart disease. For its capacity to manage high-dimensional data and choose pertinent features, a soft-margin L1-regularised Support Vector Machine (sSVM) classifier is used. The large-scale sSVM problem is successfully solved using the cluster primal-dual splitting algorithm, which improves computational complexity and scalability. The integration of federated learning provides a cooperative predictive analytics methodology that upholds data privacy. The use of a federated learning framework in this study, with a focus on peer-to-peer applications, is crucial for enabling collaborative predictive modeling while protecting the confidentiality of each participant's private medical information.
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Registered trials
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