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Journal of Medical Systems

, Volume 31, Issue 2, pp 117–123 | Cite as

Efficacy of Interferon Treatment for Chronic Hepatitis C Predicted by Feature Subset Selection and Support Vector Machine

  • Jun Yang
  • Anto Satriyo Nugroho
  • Kazunobu Yamauchi
  • Kentaro Yoshioka
  • Jiang Zheng
  • Kai Wang
  • Ken Kato
  • Susumu Kuroyanagi
  • Akira Iwata
Original Paper

Abstract

Chronic hepatitis C is a disease that is difficult to treat. At present, interferon might be the only drug, which can cure this kind of disease, but its efficacy is limited and patients face the risk of side effects and high expense, so doctors considering interferon must make a serious choice. The purpose of this study is to establish a simple model and use the clinical data to predict the interferon efficacy. This model is a combination of Feature Subset Selection and the Classifier using a Support Vector Machine (SVM). The study indicates that when five features have been selected, the identification by the SVM is as follows: the identification rate for the effective group is 85%, and the ineffective group 83%. Analysis of selected features show that HCV-RNA level, hepatobiopsy, HCV genotype, ALP and CHE are the most significant features. The results thus serve for the doctors’ reference when they make decisions regarding interferon treatment.

Keywords

Chronic Hepatitis C (CHC) Interferon (IFN) Support Vector Machine (SVM) Feature Subset Selection (FSS) Predict 

Notes

Acknowledgements

J.Y and A.S.N contributed equally to this study. The research of A.S.N is partially supported by a Grant-in-Aid for Private University High-Tech Research Center from the Ministry of Education, Culture, Sports, Science and Technology of Japan.

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Copyright information

© Springer Science+Business Media, LLC 2006

Authors and Affiliations

  • Jun Yang
    • 1
  • Anto Satriyo Nugroho
    • 2
  • Kazunobu Yamauchi
    • 1
  • Kentaro Yoshioka
    • 3
  • Jiang Zheng
    • 1
  • Kai Wang
    • 1
  • Ken Kato
    • 1
  • Susumu Kuroyanagi
    • 4
  • Akira Iwata
    • 4
  1. 1.Department of Medical Information and Management Science, Graduate School of MedicineNagoya UniversityNagoyaJapan
  2. 2.School of Life System Science and TechnologyChukyo UniversityChukyoJapan
  3. 3.Hepato-Gastroenterology, School of MedicineFujita Health UniversityFujitaJapan
  4. 4.Department of Computer Science and EngineeringNagoya Institute of TechnologyNagoyaJapan

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