| Abstract: |
This research addresses the critical need for advanced diagnostic methodologies in heart disease, a leading cause of mortality worldwide. Traditional diagnostic models, which often analyze genomic, clinical, and medical imaging data in isolation, fall short in providing a holistic understanding of the disease due to their fragmented approach. Such methods also grapple with significant challenges including data privacy concerns, lack of interpretability, and an inability to adapt to the continuously evolving landscape of medical data samples. In response, this study introduces an innovative approach known as Deep Multimodal Feature Fusion, designed to integrate genomic data, clinical history, and medical imaging into a cohesive analysis framework. This method leverages the unique strengths of each data modality, offering a more comprehensive patient profile than traditional, one-dimensional analyses. The integration of Explainable Artificial Intelligence with Clinical Data Interpretation enhances model transparency and interpretability, crucial for healthcare applications. The use of Transfer Learning with Pre-trained Models on medical imaging data and Continual Learning for Adaptive Genomics ensures diagnostic accuracy and model adaptability over temporal instance sets. Federated Learning for Privacy-Preserving Analysis is employed to address data privacy, allowing for collaborative model training without compromising patient confidentiality. Testing across diverse datasets demonstrated substantial improvements in diagnostic Precision, Accuracy, Recall, and other metrics, indicating a major advancement over existing methods. Practically, it exemplifies the application of advanced AI techniques in clinical settings, narrowing the gap between theoretical research and practical healthcare solutions. |