Computational Methods for the Early Detection of Diabetes

Computational Methods for the Early Detection of Diabetes

David J. Cornforth (University of New South Wales, Australia) and Herbert F. Jelinek (Charles Sturt University, Australia)
Copyright: © 2008 |Pages: 7
DOI: 10.4018/978-1-59904-889-5.ch037
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Abstract

The incidence of diabetes is increasing, and is expected to exceed one million people in Australia by the year 2010. Diabetes is currently diagnosed after the onset of symptoms. At this stage, detection of complications is a key intervention point in reducing the associated personal and community burden. These include eye, heart, kidney, and foot disease, which in many instances can be treated with good outcomes, provided the disease process is recognized early. In Australia, both national and state governments acknowledge the disadvantage faced by rural people in availing themselves of all aspects of diabetes management, from screening to regular assessment, education and health care (Colagiuri, Colagiuri, & Ward 1998). Therefore, the challenge that is the focus of this article is the early detection of diabetes complications associated with vision and cardiac function, with the eventual aim of providing a screening service that can be used in a rural or regional environment.

Key Terms in this Chapter

Microaneurysm: A bulge in a small blood vessel.

Diabetic Retinopathy (DR): Damage to the eye caused by diabetes.

Fluorescein: Using fluorescent dye.

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