Federated Transfer Learning Framework for Multi-disease Prediction

The research under consideration introduces a novel federated transfer learning approach to multiple disease prediction, including heart disease, kidney disease, lung disease, and liver disease. Thus, relying on the benefits of deep learning approaches and federated learning framework, the authors try to improve the accuracy of disease prediction while preserving the patients’ data privacy. Such an approach is based on transfer learning, where the model knows how to learn from one disease and apply it to another related disease to give a better prognosis of the patient’s survival rates. Federated learning is used to train the model on several distributed healthcare organizations without transferring patients’ data so that the security of data is not compromised. In addition, the framework utilizes AWS inputs related to medical data to enhance the reliability of data, and to develop the improvement of disease prediction. The proposed framework improves deep learning architectures to identify the connection and characteristics of medical databases for the early detection of diseases and their possible outcomes. The verification of the theoretical construction validates the framework in terms of attaining high overall accuracy and benefit for the patient’s unique health care services and the favored model of health care. The performance of the proposed framework is analyzed using basic metrics like the size of Nodules, GFR levels, and ALT analysis.