A Body Area Network Approach for Stroke-Related Disease Diagnosis Using Artificial Intelligence with Deep Learning Techniques

Abstract

Stroke is the second largest disease after heart disease that leads to death. Stroke-related diseases need immediate attention from health care experts. With rapid growth and advancements in the field of medical technologies across the world, there is a huge demand for the latest wireless communication technology, especially for the continuous monitoring of patients. Body area network is one among the promising technology which uses special-purpose sensor networks with design principles to function independently across various platforms to connect several medical sensors and related applications. The Body area network was applied in most of the medical and its related applications starting from basic patient monitoring systems to advanced critical disease diagnosis applications which provides a high degree of health care services not only to patients but also to health care professionals. The main objective of this research work was to propose a new medical approach for earlier disease diagnoses for stroke and its related disease that require immediate treatments. This work Integrates the body area network with artificial intelligence techniques which enables health care workers to speed up the diagnosis process that needs immediate attention. The experimental results show that the proposed approach obtained better results with an accuracy of 88.47% when compared to other existing models and also identified that this model is best suited for disease diagnosis, especially for stroke-related issues.