Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Mining Diabetes Database With Decision Trees and Association Rules
CBMS '02 Proceedings of the 15th IEEE Symposium on Computer-Based Medical Systems (CBMS'02)
Introduction to the special issue on data mining for health informatics
ACM SIGKDD Explorations Newsletter - Special issue on data mining for health informatics
Computers & Mathematics with Applications
Diabetes Data Analysis and Prediction Model Discovery Using RapidMiner
FGCN '08 Proceedings of the 2008 Second International Conference on Future Generation Communication and Networking - Volume 03
Association Rule for Classification of Type-2 Diabetic Patients
ICMLC '10 Proceedings of the 2010 Second International Conference on Machine Learning and Computing
Data mining and knowledge discovery in databases: applications in astronomy and planetary science
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
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Association Rule Discovery is a significant data mining technique. In this paper, we applied this technique to discover fundamental association among a data set of diabetes mellitus (DM) patients with ophthalmic complication using a classifier based on gender, age and payment method of treatment expense. The result indicated that "diabetes mellitus (DM) patients Type II aging between 60-69 years old with no occupation whose payment for their treatment expense was by Government Official Rights of Continuous Treatment tended to have diabetes mellitus (DM) with ophthalmic complication." This conclusion is useful for healthcare treatment of adulthood patients, welfare improvement of public healthcare, provision of helpful recommendation for diabetes mellitus patients and further development in finding disease complication.