Diagnosing diabetes illness using pervasive computing and artificial neural networks

With the developing technology, the constraints of processor speed, memory and input/output of small mobile and wireless devices such as personal digital assistants-PDAs (such as Pocket PCs, Tablet PCs, and smart phones), and sensors, which are the main user devices of pervasive healthcare computing have began to decline. The applicability of the artificial neural networks algorithms that require CPU, memory and I/O intensive operations on small mobile devices is increased in medical area. This paper presents a novel approach for diagnosing diabetes illness using pervasive healthcare computing and artificial neural networks on small mobile and wireless devices.