Federated learning-based multimodal approach for early detection and personalized care in cardiac disease.

IF 3.2 3区 医学 Q2 PHYSIOLOGY
Frontiers in Physiology Pub Date : 2025-04-23 eCollection Date: 2025-01-01 DOI:10.3389/fphys.2025.1563185
Sultan Alasmari, Rayed AlGhamdi, Ghanshyam G Tejani, Sunil Kumar Sharma, Seyed Jalaleddin Mousavirad
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引用次数: 0

Abstract

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.

基于联邦学习的多模态方法用于心脏病的早期检测和个性化护理。
心脏病仍然是全球死亡的主要原因,早期发现对于有效治疗和管理至关重要。然而,目前的诊断技术往往由于异构健康数据的错误整合而准确性较差,限制了其临床用途。方法:为了解决这一限制,我们提出了一个基于多模态数据分析和联邦学习的隐私保护框架。我们的方法使用基于注意力的特征融合模型集成了心脏图像、ECG信号、患者记录和营养数据。为了保护患者数据隐私并确保可扩展性,我们采用联合学习与使用随机梯度下降(SGD-DNN)优化的局部训练深度神经网络。将融合后的特征向量输入到SGD-DNN中进行心脏病分类。结果:提出的框架在跨多个数据集的心脏病检测中显示出很高的准确性:在数据库1上为97.76%,在数据库2上为98.43%,在数据库3上为99.12%。这些结果表明了模型的鲁棒性和泛化性。讨论:我们的框架能够实现早期诊断和个性化的生活方式建议,同时保持严格的数据机密性。联合学习和多模式特征融合的结合为心脏病管理提供了可扩展的、以隐私为中心的解决方案,具有在现实世界临床实施的强大潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.50
自引率
5.00%
发文量
2608
审稿时长
14 weeks
期刊介绍: Frontiers in Physiology is a leading journal in its field, publishing rigorously peer-reviewed research on the physiology of living systems, from the subcellular and molecular domains to the intact organism, and its interaction with the environment. Field Chief Editor George E. Billman at the Ohio State University Columbus is supported by an outstanding Editorial Board of international researchers. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics, clinicians and the public worldwide.
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