TITLE:
Comparison of Various Classification Techniques Using Different Data Mining Tools for Diabetes Diagnosis
AUTHORS:
Rashedur M. Rahman, Farhana Afroz
KEYWORDS:
Classification; Neural Network; Fuzzy Logic; Decision Tree; Performance Measurement
JOURNAL NAME:
Journal of Software Engineering and Applications,
Vol.6 No.3,
March
29,
2013
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.