TY - GEN
T1 - Data mining technique for medical diagnosis using a new smooth support vector machine
AU - Purnami, Santi Wulan
AU - Zain, Jasni Mohamad
AU - Embong, Abdullah
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
KW - classification
KW - data mining technique
KW - medical diagnosis
KW - multiple knot spline function
KW - smooth support vector machine
UR - http://www.scopus.com/inward/record.url?scp=77956105367&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-14306-9_3
DO - 10.1007/978-3-642-14306-9_3
M3 - Conference contribution
AN - SCOPUS:77956105367
SN - 3642143059
SN - 9783642143052
T3 - Communications in Computer and Information Science
SP - 15
EP - 27
BT - Networked Digital Technologies - Second International Conference, NDT 2010, Proceedings
T2 - 2nd International Conference on 'Networked Digital Technologies', NDT 2010
Y2 - 7 July 2010 through 9 July 2010
ER -