Explainable Deep Learning Framework for ECG-Based Cardiovascular Disease Detection with Secure Encrypted Medical Data Transmission

An explainable deep learning framework for ECG-based cardiovascular disease detection with secure, encrypted medical data transmission.

An explainable deep learning framework for ECG-based cardiovascular disease detection with secure, encrypted medical data transmission. As a reference project it shows how a system like this is put together in practice, which parts matter and how they are tested, so that a developer can learn from it and build on it.

It suits learners who want to work with neural networks end to end: preparing datasets, designing and training a model, tuning it, evaluating it properly and explaining the outcome.

The main parts are explanations of each result (feature importance or visual evidence), security and privacy controls such as encryption and access control, dataset preparation and augmentation, model design and training, and evaluation with standard metrics, plus error analysis. The reference build uses SHAP, Python, PyTorch, TensorFlow, and NumPy, with a clear project structure, setup instructions and sample data so that it can be run and extended.