Kavitha

Kavitha, K. et al. 2012, 1 have designed an approach to enhance effectiveness and efficiency of health monitoring using CART data mining tool for early detection of diabetes. The proposed system has achieved accuracy and a precision of 96.39% and 100.00% to detect Diabetes
Aljumah, A.,A. et al. 2013, 2 have used regression based data mining technique and Oracle data mining tool for treatment of young and old age group diabetic patients. For experimental analysis the support vector machine algorithm was used and finally concluded that the drug treatment was delayed to avoid side effects in young age group patients. The drug treatment was immediately start to the patients of old age group because there were no other options available.
Singh, S., K. and Archana, 2013, 3 proposed an intelligent and useful methodology for the automatic detection of Diabetes. This methodology was designed using Artificial Neural Network technique and implemented on MATLAB software. In proposed model, user itself sitting on home can diagnose whether they were suffering from Diabetes or not. They only need to provide some physical parameters to the proposed model then it was detected whether that person was suffering from Diabetes or not.
Rahman, R., M. et al. 2013, 4 compared the different classification techniques using three data mining tools named WEKA, TANAGRA and MATLAB on diabetes data. The result showed that the best algorithm in WEKA was J48graft classifier with an accuracy of 81.33% that takes 0.135 seconds for training. In TANAGRA, Naïve Bayes classifier provided accuracy of 100% with training time 0.001 seconds. In MATLAB, ANFIS had 78.79% accuracy. They concluded that the TANAGRA tool was best as compared to WEKA and MATLAB.
Rajesh, K. and Sangeetha, V. 2012, 5 found the appropriate data mining methods and techniques for classification of diabetes dataset. The result showed that C4.5 classification algorithm efficiency rate was 91% than others .