Privacy-Preserving Heart Disease Prediction Using Graph Neural Networks, Federated Learning and Quantum Optimization

A privacy-preserving heart disease prediction system that uses graph neural networks, federated learning and quantum optimisation.

A privacy-preserving heart disease prediction system that uses graph neural networks, federated learning and quantum optimisation. 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 hands-on experience of the whole machine learning workflow: data collection and cleaning, feature engineering, model training and tuning, evaluation and presenting results.

The main parts are privacy-preserving training across several clients, with only model updates shared, security and privacy controls such as encryption and access control, data preparation and exploration, model training and comparison of algorithms, and evaluation with standard metrics. The reference build uses TensorFlow, Flower, PyTorch Geometric, Qiskit, and Python, with a clear project structure, setup instructions and sample data so that it can be run and extended.