Data mining technique for medical diagnosis using a new smooth support vector machine

Santi Wulan Purnami, Jasni Mohamad Zain, Abdullah Embong

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationNetworked Digital Technologies - Second International Conference, NDT 2010, Proceedings
Pages15-27
Number of pages13
EditionPART 2
DOIs
Publication statusPublished - 2010
Event2nd International Conference on 'Networked Digital Technologies', NDT 2010 - Prague, Czech Republic
Duration: 7 Jul 20109 Jul 2010

Publication series

NameCommunications in Computer and Information Science
NumberPART 2
Volume88 CCIS
ISSN (Print)1865-0929

Conference

Conference2nd International Conference on 'Networked Digital Technologies', NDT 2010
Country/TerritoryCzech Republic
CityPrague
Period7/07/109/07/10

Keywords

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

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