Abstract
In last decade, the uses of data mining techniques in medical studies are growing gradually. The aim of this paper is to present a recent research on the application of data mining technique for medical diagnosis problems. The proposed data mining technique is Multiple Knot Spline Smooth Support Vector Machine (MKS-SSVM). MKS-SSVM is a new SSVM which used multiple knot spline function to approximate the plus function instead the integral sigmoid function in SSVM. To evaluate the effectiveness of our method, we carried out on two medical dataset (diabetes disease and heart disease). The accuracy of previous results of these data still under 90% so far. The results of this study showed that MKS-SSVM was effective to diagnose medical dataset, especially diabetes disease and heart disease 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). Data Mining Technique for Medical Diagnosis Using a New Smooth Support Vector Machine. In: Zavoral, F., Yaghob, J., Pichappan, P., El-Qawasmeh, E. (eds) Networked Digital Technologies. NDT 2010. Communications in Computer and Information Science, vol 88. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14306-9_3
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DOI: https://doi.org/10.1007/978-3-642-14306-9_3
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