Comparative Study and Analysis of Classification Algorithms In Data Mining Using Diabetic Dataset

Authors(2) :-R. S. Suryakirani, R. Porkodi

Classification is use to categorize each item in a set of data into one of predefined set of module or grouping. The data analysis task classification is where a model or classifier is constructed to predict categorical labels. The goal of the classification is to accurately predict the target class for each case in the data. The field of data mining due to its enormous success in terms of broad ranging application achievements and scientific progress, understanding. Many data mining application have been successfully implemented in various domains like healthcare, finance, retail, telecommunication, fraud detection and risk analysis etc. This paper presents the study and analysis of four classification algorithms namely J48, Random tree, Decision tree and Naive Bayes for Diabetic dataset and the performance are compared using the measures such as computing time, Correctly Classified Instances, Incorrectly Classified Instances, kappa statistics, Precision, Recall and F measure. The experimental result shows that J48 provides better accuracy than the Random tree, Decision tree and Naive Bayes.

Authors and Affiliations

R. S. Suryakirani
PG Scholar, Department of Computer Science, Bharathiar University, Coimbatore, Tamilnadu, India
R. Porkodi
Assistant Professor, Department of Computer Science, Bharathiar University, Coimbatore, Tamilnadu, India

Classification Algorithms, Naive Bayes, Random Tree, Decision Tree, J48.

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Publication Details

Published in : Volume 4 | Issue 2 | January-February 2018
Date of Publication : 2018-02-28
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 299-304
Manuscript Number : IJSRST1184139
Publisher : Technoscience Academy

Print ISSN : 2395-6011, Online ISSN : 2395-602X

Cite This Article :

R. S. Suryakirani, R. Porkodi, " Comparative Study and Analysis of Classification Algorithms In Data Mining Using Diabetic Dataset", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 4, Issue 2, pp.299-304, January-February-2018.
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