A new expert system for diabetes disease diagnosis using modified spline smooth support vector machine

Authors:
Santi Wulan Purnami;Jasni Mohamad Zain;Abdullah Embong
Affiliations:
Faculty of Computer System and Software Engineering, University Malaysia Pahang;Faculty of Computer System and Software Engineering, University Malaysia Pahang;Faculty of Computer System and Software Engineering, University Malaysia Pahang
Venue:
ICCSA'10 Proceedings of the 2010 international conference on Computational Science and Its Applications - Volume Part IV
Year:
2010

Citing 6
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Abstract

In recent years, the uses of intelligent methods in biomedical studies are growing gradually. In this paper, a novel method for diabetes disease diagnosis using modified spline smooth support vector machine (MS-SSVM) is presented. To obtain optimal accuracy results, we used Uniform Design method for selection parameter. The performance of the method is evaluated using 10-fold cross validation accuracy, confusion matrix, sensitivity and specificity. The comparison with previous spline SSVM in diabetes disease diagnosis also was given. The obtained classification accuracy using 10-fold cross validation is 96.58%. The results of this study showed that the modified spline SSVM was effective to detect diabetes disease diagnosis and this is very promising result compared to the previously reported results.