Federated Edge-Cloud Framework for Heart Disease Risk Prediction Using Blockchain

Heart disease is a term used to describe a range of conditions that affect the heart, such as coronary artery disease, heart failure, and arrhythmias. It is a leading cause of death globally and can be prevented or managed through lifestyle changes and medical treatments, such as medication and surgery. The use of federated learning and edge computing has become increasingly popular for machine learning tasks, especially in the healthcare domain. However, privacy and security concerns remain major challenges in the adoption of such technologies. In this article, we propose a blockchain-enabled federated edge-cloud framework for heart disease risk prediction to address these challenges. The proposed framework involves the use of blockchain to secure the data sharing and model aggregation process, while edge devices are utilized for data preprocessing and feature extraction, and cloud servers are used for model training and validation. The federated learning approach ensures data privacy, while the use of blockchain provides immutability, transparency, and accountability to the system.