Comparison of Various Classification Techniques Using Different Data Mining Tools for Diabetes Diagnosis

Abstract

In the absence of medical diagnosis evidences, it is difficult for the experts to opine about the grade of disease with affirmation. Generally many tests are done that involve clustering or classification of large scale data. However many tests could complicate the main diagnosis process and lead to the difficulty in obtaining the end results, particularly in the case where many tests are performed. This kind of difficulty could be resolved with the aid of machine learning techniques. In this research, we present a comparative study of different classification techniques using three data mining tools named WEKA, TANAGRA and MATLAB. The aim of this paper is to analyze the performance of different classification techniques for a set of large data. A fundamental review on the selected techniques is presented for introduction purpose. The diabetes data with a total instance of 768 and 9 attributes (8 for input and 1 for output) will be used to test and justify the differences between the classification methods. Subsequently, the classification technique that has the potential to significantly improve the common or conventional methods will be suggested for use in large scale data, bioinformatics or other general applications.