期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2014
卷号：68
期号：1
出版社：Journal of Theoretical and Applied
摘要：Diabetes is a chronic disease and a major public health challenge worldwide. According to the International Diabetes Federation, there are currently 246 million diabetic people worldwide, and this number is expected to rise to 430 million by 2030. Furthermore, 3.8 million deaths are attributable to diabetes complications each year. It has been shown that 80% of type 2 diabetes complications can be prevented or delayed by early identification of people at risk. Early detection of diabetes would be of great value given the fact that at least 50% and 80% in some countries, of all people with diabetes are unaware of their condition and will remain unaware until complications appear. Several data mining and machine learning methods have been used for the diagnosis, prognosis, and management of diabetes. In my research work, clinical data are collected based on the attributes downloaded from Pima Indians Diabetes Database. Real time input have been given to the system from the Glucometer (ie Glucose level for the patient before breakfast and Glucose level for the patient two hours after breakfast) then some on the input attributes given manually to the system. All the inputs have been given to the BP Algorithm (ANN) and Apriori Algorithm (ARM) for diagnosing diabetes. The Diagnosis system can be implemented in Java, Dotnet. The output of the system shows how much percentage of the patient suffers from the diabetes (low, medium or high risks)? In proposed work it is decided to implement the system in Online. The main objective is that the patients can know about their risk in diabetes without the help of doctors. The patient can just login into the website and they can give their attributes (ie) data collected from labs as input and they can diagnosis their own result without doctors.
关键词：Artificial Neural Network (ANN); Back Propagation Algorithm; Association Rule Mining (ARM); Apriori Algorithm; Disease Diagnosis.
