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Updated: May 12, 2025
Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
Published on: January 8, 2013
Federated learning-based multimodal approach for early detection and personalized care in cardiac disease
Sultan Alasmari1,2, Rayed AlGhamdi3, Ghanshyam G Tejani4,5
1Department of Information Systems, College of Computer and Information Sciences, Majmaah University, Majmaah, Saudi Arabia.
Insights
This study introduces a novel framework for accurate heart disease detection using multimodal data and federated learning. The approach ensures privacy and achieves high diagnostic accuracy, paving the way for better heart health management.
Area of Science:
- Cardiology
- Artificial Intelligence
- Data Science
Background:
- Heart disease is a leading global cause of mortality.
- Current diagnostic methods lack accuracy due to data integration challenges.
- Early detection is crucial for effective heart disease management.
Purpose of the Study:
- To develop a privacy-preserving framework for accurate heart disease detection.
- To integrate heterogeneous health data for improved diagnostic capabilities.
- To enable early diagnosis and personalized lifestyle recommendations.
Main Methods:
- Utilized a multimodal data analysis framework integrating cardiac images, ECG signals, patient records, and nutrition data.
- Employed an attention-based feature fusion model for data integration.
- Implemented federated learning with locally trained Deep Neural Networks (SGD-DNN) for privacy-preserving classification.
Main Results:
- Achieved high accuracy in cardiac disease detection: 97.76% on Database 1, 98.43% on Database 2, and 99.12% on Database 3.
- Demonstrated the robustness and generalizability of the proposed framework across multiple datasets.
- Validated the effectiveness of multimodal feature fusion and federated learning for cardiac diagnostics.
Conclusions:
- The proposed framework offers a scalable, privacy-centric solution for heart disease management.
- Enables early diagnosis and personalized lifestyle recommendations while ensuring data confidentiality.
- Shows strong potential for real-world clinical implementation in cardiovascular care.
Introduction:
Heart disease remains a leading cause of mortality globally, and early detection is critical for effective treatment and management. However, current diagnostic techniques often suffer from poor accuracy due to misintegration of heterogeneous health data, limiting their clinical usefulness.
Methods:
To address this limitation, we propose a privacy-preserving framework based on multimodal data analysis and federated learning. Our approach integrates cardiac images, ECG signals, patient records, and nutrition data using an attention-based feature fusion model. To preserve patient data privacy and ensure scalability, we employ federated learning with locally trained Deep Neural Networks optimized using Stochastic Gradient Descent (SGD-DNN). The fused feature vectors are input into the SGD-DNN for cardiac disease classification.
Results:
The proposed framework demonstrates high accuracy in cardiac disease detection across multiple datasets: 97.76% on Database 1, 98.43% on Database 2, and 99.12% on Database 3. These results indicate the robustness and generalizability of the model.
Discussion:
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.
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