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