EDCF2SL: Design of an Explainable Deep Learning Model for Cardiovascular Disease Analysis using Federated Learning with Few-Shot Learning Operations

Abstract

The timely detection and pre-emption of cardiovascular diseases (CVDs) remain pivotal challenges in healthcare, necessitating innovative approaches in signal analysis and machine learning. Existing methodologies often fall short in precision, accuracy, and timeliness, underscoring the need for more sophisticated and integrated solutions. This paper presents a novel framework employing advanced deep learning architectures and federated learning techniques for enhanced      photoplethysmography (PPG) signal analysis. Our approach integrates transformer networks and capsule networks to effectively capture temporal dependencies and spatial  hierarchies in multidimensional PPG data, addressing limitations in current practices by significantly improving the precision and accuracy of the CVD detection process. We incorporate federated averaging algorithms and secure aggregation protocols to train models across multiple devices while ensuring data privacy levels. Further, our  methodology leverages interpretable Deep SHAP, providing clarity and transparency in model decisions, a critical factor in clinical settings. The integration of multi-modal data through multiple input convolutional neural networks and recurrent neural networks with (LSTM) and bidirectional gated recurrent unit (BiGRU) networks allows for a comprehensive analysis of varied physiological signals. Additionally, our model employs sophisticated  nomaly detection techniques, including autoencoders and Isolation Forest, for early and precise identification of unusual patterns in  PG signals. To cater to individual variances in physiological signals, we implement personalized and adaptive models using model-agnostic meta-learning with few-shot learning, ensuring tailored detection and monitoring processes. The unique blend of machine learning models and rulebased systems through ensemble methods further enhances the efficacy of our framework. Clinical testing across multiple heart diseases has demonstrated the superiority of our approach, showing significant improvements over existing methods in various metrics such as precision, accuracy, recall, aUC, specificity, and response delays. This work not only marks a significant advancement in the detection and pre-emption of CVDs but also sets a new benchmark for the application in medical diagnostics, promising substantial impacts on patient outcomes and healthcare practices.