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Data Mining Technique for Medical Diagnosis Using a New Smooth Support Vector Machine

  • Santi Wulan Purnami
  • Jasni Mohamad Zain
  • Abdullah Embong
Part of the Communications in Computer and Information Science book series (CCIS, volume 88)

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.

Keywords

data mining technique classification medical diagnosis smooth support vector machine multiple knot spline function 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Santi Wulan Purnami
    • 1
    • 2
  • Jasni Mohamad Zain
    • 1
  • Abdullah Embong
    • 1
  1. 1.Faculty of Computer System and Software EngineeringUniversity Malaysia PahangKuantan PahangMalaysia
  2. 2.Department of StatisticsInstitut Teknologi Sepuluh Nopember (ITS) SurabayaSurabayaIndonesia

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