Accuracy comparison of the data mining classification techniques for the diabetic disease prediction

In the present scenario, the speedy use of the data mining (DM) techniques is observed for predicting and categorising symptoms in large medical datasets. Classification is one major DM technique that is widely used for classifying various unnoticed information from various diagnostic data. In a popular country like India, diabetes is characterised as a dangerous disease which has affected the majority of the population. The present research emphasises on the accuracy comparison of the various classifiers such as J48, random forest, sequential minimal optimisation (SMO), stochastic gradient descent (SGD), naive Bayes, logistic regression, random tree, decision stump, simple logistic, Hoeffding tree, Adaboost, and bagging, when applied to diabetic data.