Diagnosing diabetes using neural networks on small mobile devices

Pervasive computing is often mentioned in the context of improving healthcare. This paper presents a novel approach for diagnosing diabetes using neural networks and pervasive healthcare computing technologies. The recent developments in small mobile devices and wireless communications provide a strong motivation to develop new software techniques and mobile services for pervasive healthcare computing. A distributed end-to-end pervasive healthcare system utilizing neural network computations for diagnosing illnesses was developed. This work presents the initial results for a simple client (patient’s PDA) and server (powerful desktop PC) two-tier pervasive healthcare architecture. The computations of neural network operations on both client and server sides and wireless network communications between them are optimized for real time use of pervasive healthcare services.

► A novel approach is presented for a client (patient’s hand-held small mobile device, PDA) and server (powerful desktop PC) two-tier pervasive healthcare architecture.
► The computational model and the client and server applications presented are based on artificial neural network (ANN) techniques.
► The computations of ANN operations on both client and server sides and wireless network communications between them are optimized for real time use of pervasive healthcare services depending on the nature of illnesses.
► The client mobile application tries to make its ANN and other complex calculations locally and shows the results to the patient without contacting to the server for a range of illnesses including diabetes.
► The main questions answered are how a client PDA can share the ANN computation load with a server and become a data classifier, if the PDA classifier has a good accuracy, and if the PDA and server can do computations for diagnosing illnesses in real time.