Hybrid Deep Transfer Learning Framework for Stroke Risk Prediction

Stroke has become a leading cause of death and long-term disability in the world with no effective treatment. Deep learning-based approaches have the potential to outperform existing stroke risk prediction models. Due to the strict privacy protection policy in health-care systems, stroke data is usually distributed among different hospitals in small pieces. Transfer learning can solve small data issue by exploiting the knowledge of a correlated domain, especially when multiple source of data are available. In this work, we propose a novel Hybrid Deep Transfer Learning-based Stroke Risk Prediction scheme.