A Primary Study for Cancer Prognosis based on Classification and Regression Using Support Vector Machine

  • Jia Qinan
  • Ma Lei
  • He Jianfeng
  • Yi QingQing
  • Zhang Jun
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 269)


In medical domain, prognosis prediction treated as a regression problem is generally applied to predict the event duration time, such as the duration time of the recurrence of a certain disease. Recently, machine learning techniques are gaining popularity in this field because of its effectiveness and reliability. In this paper, a method based on support vector machine (SVM) to predict the exact recurrence time has been proposed. The method is compared with other four prognostic methods using Wisconsin Breast Cancer Dataset. Experimental results demonstrate that the method is more simplified to be implemented than the other four prognostic methods, and it performs much better than the medium level.


Prognosis prediction Event time Support vector machine 



The authors would like to thank professor Li’s at University of South Australia for his constructive comments. This project is supported by national natural science funding of China (project number: 11265007).


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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Jia Qinan
    • 1
  • Ma Lei
    • 1
  • He Jianfeng
    • 1
  • Yi QingQing
    • 1
  • Zhang Jun
    • 1
  1. 1.Department of Biomedical EngineeringKunming University of Science and TechnologyKunmingPeople’s Republic of China

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