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.
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Purnami, S.W., Zain, J.M., Embong, A. (2010). A New Expert System for Diabetes Disease Diagnosis Using Modified Spline Smooth Support Vector Machine. In: Taniar, D., Gervasi, O., Murgante, B., Pardede, E., Apduhan, B.O. (eds) Computational Science and Its Applications – ICCSA 2010. ICCSA 2010. Lecture Notes in Computer Science, vol 6019. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12189-0_8
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DOI: https://doi.org/10.1007/978-3-642-12189-0_8
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