Comparative of Data Mining Classification Algorithm (CDMCA) in Diabetes Disease Prediction
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10.5120/9745-4307 |
V Karthikeyani, Parvin I Begum, K Tajudin and Shahina I Begam. Article: Comparative of Data Mining Classification Algorithm (CDMCA) in Diabetes Disease Prediction. International Journal of Computer Applications 60(12):26-31, December 2012. Full text available. BibTeX
@article{key:article,
author = {V. Karthikeyani and I. Parvin Begum and K. Tajudin and I. Shahina Begam},
title = {Article: Comparative of Data Mining Classification Algorithm (CDMCA) in Diabetes Disease Prediction},
journal = {International Journal of Computer Applications},
year = {2012},
volume = {60},
number = {12},
pages = {26-31},
month = {December},
note = {Full text available}
}
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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