Transfer Learning and Multi-Task Optimization for Multi-Modal Heart Disease Prediction: Integrating Clinical Data and ECG Signals with Uncertainty Quantification

Key Points

• The aim is to enhance heart disease prediction models by integrating clinical data and ECG signals with uncertainty quantification.
• Utilized transfer learning and multi-task optimization in model development.
• Combined clinical data and ECG signals for predictive analysis.
• Implemented uncertainty quantification to assess model reliability.
• Achieved an increase in prediction accuracy by 15% compared to traditional models.
• Demonstrated a significant reduction in uncertainty during predictions with a confidence interval of 95%.