Using Stratified Sample and Grid Search to Improve Disease Prediction Accuracy of SVM

Yu Kai Yao; Hong Mei Cui; Ming Wei Len; Xiao Yun Chen

doi:10.4028/www.scientific.net/AMM.295-298.644

Paper Titles

Offshore Oil Spill Incidents in Brazil
p.626

Characterization of Antibiotic Resistance E. Coli and Antibiotic Resistance Genes in Aquatic Environment of Taihu Lake, China
p.630

Researches on the Forewarning Indicator System of Groundwater Environmental Health
p.635

Effects of Phthalic Acid Esters on the Ecological Environment and Human Health
p.640

Using Stratified Sample and Grid Search to Improve Disease Prediction Accuracy of SVM
p.644

Integrating Environmental Assessment of Failure Modes in Maintenance Planning of Production Systems
p.651

The Ecological Water Use Efficiency Evaluation of City’s Riverway
p.661

Estimation of the Environmental Load Caused by Introducing Light Rail Transit Using an Urban Transportation Simulator
p.669

Assessment of Regional Geological Disaster on Grey Clustering Methed in Shulan
p.673

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 295-298Using Stratified Sample and Grid Search to Improve...

Using Stratified Sample and Grid Search to Improve Disease Prediction Accuracy of SVM

Abstract:

SVM (Support Vector Machine) is a powerful data mining algorithm, and is mainly used to finish classification or regression tasks. In this literature, SVM is used to conduct disease prediction. We focus on integrating with stratified sample and grid search technology to improve the classification accuracy of SVM, thus, we propose an improved algorithm named SGSVM: Stratified sample and Grid search based SVM. To testify the performance of SGSVM, heart-disease data from UCI are used in our experiment, and the results show SGSVM has obvious improvement in classification accuracy, and this is very valuable especially in disease prediction.

You might also be interested in these eBooks

Progress in Environmental Protection and Processing of Resource

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 295-298)

Pages:

644-647

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.295-298.644

Citation:

Cite this paper

Online since:

February 2013

Authors:

Yu Kai Yao, Hong Mei Cui, Ming Wei Len, Xiao Yun Chen

Keywords:

Disease Prediction, Grid Search, Kernel Function, Stratified Sample, Support Vector Machine (SVM)

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] L. H. Witten, E. Frank, M. A. Hall: Data Mining: practical machine learning tools and techniques. -3rd ed. (Elsevier, Burlington 2011).

DOI: 10.1016/b978-0-12-374856-0.00015-8

Google Scholar

[2] X. D. Wu, V. Kumar: The Top Ten Algorithms in Data Mining (CRC Press, New York 2009).

Google Scholar

[3] V. Vapnik. The Nature of Statistical Learning Theory. (Springer, Verlag 1995).

Google Scholar

[4] V. vapnik., Statistical Learning Theory. (Wiley, New York 1998).

Google Scholar

[5] H.L. Chen, B. Yang, G. Wang: J Med Syst. 36: 2505-2519 (2012).

Google Scholar

[6] A.K. Hens, M.K. Tiwari: Expert Systems with Applications, 39(2012) 6774-6781. Fig. 2 Fig. 3 Table 2. Ten times classification accuracy results corresponding to stratified sample and random sample based on grid search technology.

Google Scholar