Performance Analysis of Classifier Models to Predict Diabetes Mellitus

Diabetes is one of the common and growing diseases in several countries and all of them are working to prevent this disease at early stage by predicting the symptoms of diabetes using several methods. The main aim of this study is to compare the performance of algorithms those are used to predict diabetes using data mining techniques. In this paper we compare machine learning classifiers (J48 Decision Tree, K-Nearest Neighbors, and Random Forest, Support Vector Machines) to classify patients with diabetes mellitus. These approaches have been tested with data samples downloaded from UCI machine learning data repository. The performances of the algorithms have been measured in both the cases i.e dataset with noisy data (before pre-processing) and dataset set without noisy data (after pre-processing) and compared in terms of Accuracy, Sensitivity, and Specificity.