Performance analysis of data mining classification algorithms for early prediction of diabetes mellitus 2

Abstract

Diabetes mellitus (DM) generally referred to as diabetes. It is a group of metabolic infection in which there are high blood sugar levels over a prolonged period. Data mining is used for predicting various diseases. From many methods of data mining, classification is one of the main techniques. The classification techniques are used to classify the hidden information in all areas including medical diagnostic field. In this research work, we compare the machine learning classifiers (naïve Bayes, J48 decision tree, OneR, AdaBoost, random forest, random tree and support vector machines) to classify the patients into diabetic and non-diabetic mellitus. These algorithms have been tested with data samples downloaded from UCI. The performances of the algorithms have been considered in both the cases, i.e., data samples with noisy data and data samples set without noisy data. Results are evaluated in terms of accuracy, sensitivity, and specificity. Experimental results suggested that, support vector machine (SVM) classifier is the best classifier for predicting diabetes mellitus 2.