Deep Transfer Learning Based Stroke Risk Prediction

Abstract

Abstract: Stroke is a medical condition that occurs when there is any blockage or bleeding of the blood vessels either interrupts or reduces the supply of blood to the brain. It causes the disability of multiple organs or unexpected death, if that patient’s recognise and address risks at the right time, up to 80% of stroke occurrences can be averted. With the advancement of machine learning in medical science, the early recognition of stroke is very much possible that plays a vital role in diagnosis and getting read of this life taking disease. But it requires large well-labeled data. Due to the strict privacy protection policy in health-care systems, stroke data is usually distributed among different hospitals in small pieces. In addition, the positive and negative instances of such data are extremely imbalanced. Transfer learning can solve small data issue by exploiting the knowledge of a correlated domain, especially when multiple sources of data are available. In this work, deep Transfer Learning based Stroke Risk Prediction scheme is proposed to exploit the knowledge structure from multiple correlated sources and used bayesian optimization for selecting the best parameter. This model is tested in synthetic and real-world scenarios and the random forest gives more accuracy than other models like support vector machines (SVM), decision trees (DT), random forests (RF) and voting classifiers. It also shows the potential of real-world deployment among multiple hospitals aided with 5 G/B5G infrastructures.