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Comparative of Data Mining Classification Algorithm (CDMCA) in Diabetes Disease Prediction

by V. Karthikeyani, I. Parvin Begum, K. Tajudin, I. Shahina Begam
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 60 - Number 12
Year of Publication: 2012
Authors: V. Karthikeyani, I. Parvin Begum, K. Tajudin, I. Shahina Begam
10.5120/9745-4307

V. Karthikeyani, I. Parvin Begum, K. Tajudin, I. Shahina Begam . Comparative of Data Mining Classification Algorithm (CDMCA) in Diabetes Disease Prediction. International Journal of Computer Applications. 60, 12 ( December 2012), 26-31. DOI=10.5120/9745-4307

@article{ 10.5120/9745-4307,
author = { V. Karthikeyani, I. Parvin Begum, K. Tajudin, I. Shahina Begam },
title = { Comparative of Data Mining Classification Algorithm (CDMCA) in Diabetes Disease Prediction },
journal = { International Journal of Computer Applications },
issue_date = { December 2012 },
volume = { 60 },
number = { 12 },
month = { December },
year = { 2012 },
issn = { 0975-8887 },
pages = { 26-31 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume60/number12/9745-4307/ },
doi = { 10.5120/9745-4307 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:07:20.266790+05:30
%A V. Karthikeyani
%A I. Parvin Begum
%A K. Tajudin
%A I. Shahina Begam
%T Comparative of Data Mining Classification Algorithm (CDMCA) in Diabetes Disease Prediction
%J International Journal of Computer Applications
%@ 0975-8887
%V 60
%N 12
%P 26-31
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Data mining is an iterative development within which evolution is defined by discovery, through either usual or manual methods. In this paper using the data mining concept to CDMCA classifies two types supervised and unsupervised classifications. Here illustrate the classification of supervised data mining algorithms base on diabetes disease dataset. It encompass the diseases plasma glucose at least mentioned value. The research describes algorithmic discussion of C4. 5, SVM, K-NN, PNN, BLR, MLR, CRT, CS-CRT, PLS-DA and PLS-LDA. Here used to compare the performance of computing time, precision value and the data evaluated using 10 fold Cross Validation error rate, the error rate focuses True Positive, True Negative, False Positive and False Negative and Accuracy. The outcome CS-CRT algorithm best. The Best results are achieved by using Tanagra tool. Tanagra is data mining matching set. The accuracy is calculate based on addition of true positive and true negative followed by the division of all possibilities.

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Index Terms

Computer Science
Information Sciences

Keywords

C4. 5 SVM K-NN PNN BLR MLR CRT CS-CRT PLS-DA PLS-LDA Classification based on CT Precision value CV error rate and Accuracy

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