An-Architecture-for-a-Fully-Automated-Real-Time-Web-Centric-Expert-System (original) (raw)
Diabetes is the single most important metabolic disease, which can affect nearly every organ system in the body. It has been projected that 300 million individuals would be afflicted with diabetes by the year 2025. In India, it is estimated that this deadly disease affects presently 19.4 million individuals. The figure is likely to go up to 57.2 million by the year 2025. However, access to medical care is sometimes very difficult for people living in rural and underserved areas. They have to trek long distances for a medical diagnosis. Even if medical facilities are available in urban areas, due to lack of time due to work schedule, time taken to go in for this comprehensive procedures of getting appointment and spending a whole day in the labs for screening are a woeful experience for many and in some cases, the elderly people may have no one to take them to the hospital for screening. We propose a medical network based on state-of-the-art medical kiosk that addresses the problems of providing preventive and diagnostic health care. The patients can directly enter and can be screened for diabetics with all the necessary tests that are fully automated. The web centric diabetic expert system runs on a Tele health server and is connected to the kiosk through the WWW. Based on the symptoms, tests taken and previous history of the patient, a prescription is generated by the expert system that is also sent to a hospital where the doctor is online. Any changes required can be made by the physician in the diagnosis and prescription generated by the expert system and sent to the patient at the kiosk end as a printout. This paper presents the architecture of a web-based telehealth system employing expert system rules to detect for a fully automatic diabetes screening system. Different kinds diabetes and focuses on the architecture of corresponding web applications. The types of diabetes that can be detected with this system are type1, type2 and gestational diabetes. The project was designed and programmed via the dot net framework. The expert rules were developed based on the symptoms of each type of diabetes. The expert system described in this paper is able to detect and give early diagnosis of three types of diabetes namely type 1,2, gestational diabetes for both adult and children.
Sign up for access to the world's latest research.
checkGet notified about relevant papers
checkSave papers to use in your research
checkJoin the discussion with peers
checkTrack your impact
Loading Preview
Sorry, preview is currently unavailable. You can download the paper by clicking the button above.