Performance Analysis of Data Mining Classification Techniques to Predict Diabetes

Abstract Diabetes Mellitus is one of the major health challenges all over the world. The prevalence of diabetes is increasing at a fast pace, deteriorating human, economic and social fabric. Prevention and prediction of diabetes mellitus is increasingly gaining interest in healthcare community. Although several clinical decision support systems have been proposed that incorporate several data mining techniques for diabetes prediction and course of progression. These conventional systems are typically based either just on a single classifier or a plain combination thereof. Recently extensive endeavors are being made for improving the accuracy of such systems using ensemble classifiers. This study follows the adaboost and bagging ensemble techniques using J48 (c4.5) decision tree as a base learner along with standalone data mining technique J48 to classify patients with diabetes mellitus using diabetes risk factors. This classification is done across three different ordinal adults groups in Canadian Primary Care Sentinel Surveillance network. Experimental result shows that, overall performance of adaboost ensemble method is better than bagging as well as standalone J48 decision tree.