Utilization of Data Mining Techniques in Knowledge Extraction for Diminution of Diabetes

This research uses association rule generation and classification techniques to support decision making, by considering a data set of diabetes type 1 & type 2 patients. There are advanced and reliable data mining techniques which leads to the discovery of unseen and useful information. The main focus of this research is to identify the yet undiscovered decision factors of diabetes which increases the possibility of the onset of diabetes, as well as to identify the undiscovered consequences of diabetes. Through the data mining analysis, gender female is identified as a major decision factor of high FBS (Fasting Blood Sugar) level. Furthermore wheezes and edema were identified as unknown side effects of diabetes.