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Support Vector Machine-Based Feature Selection for Classification of Liver Fibrosis Grade in Chronic Hepatitis C

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Abstract

Although liver biopsy is currently regarded as the gold standard for staging liver fibrosis in chronic hepatitis C, it is a costly invasive procedure and carries a small risk for complication. Our aim in this study was to construct a simple model to distinguish between patients with no or mild fibrosis (METAVIR F0–F1) versus those with clinically significant fibrosis (METAVIR F2–F4). We retrospectively studied 204 consecutive CHC patients. Thirty-four serum markers with age, gender, duration of infection were assessed to classify fibrosis with a classifier known as the support vector machine (SVM). The method of feature selection known as sequential forward floating selection (SFFS) was introduced before the performance of SVM. When four serum markers were extracted with SFFS-SVM, F2–F4 could be predicted accurately in 96%. Our study showed that application of this model could identify CHC patients with clinically significant fibrosis with a high degree of accuracy and may decrease the need for liver biopsy.

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Abbreviations

HCV:

hepatitis C virus

CHC:

chronic hepatitis C

SVM:

support vector machine

SFFS:

sequential forward floating selection

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Acknowledgements

This work is supported by Iwata-Lab Department of Electrical and Computer Engineering, Nagoya Institute of Technology. The authors are thankful to the Hori Information Science Promotion Foundation for financial assistance.

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Authors and Affiliations

  1. Department of Medical Information and Management Science, Graduate School of Medicine, Nagoya University, 65, Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan

    Zheng Jiang, Kazunobu Yamauchi, Jun Yang & Kai Wang

  2. Department of Hepato-Gastroenterology, Graduate School of Medicine, Nagoya University, Nagoya, 466-8550, Japan

    Kentaro Yoshioka

  3. Iwata-Lab Department of Electrical and Computer Engineering, Nagoya Institute of Technology, Nagoya, Japan

    Kazuma Aoki, Susumu Kuroyanagi & Akira Iwata

Authors
  1. Zheng Jiang
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  2. Kazunobu Yamauchi
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  3. Kentaro Yoshioka
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  4. Kazuma Aoki
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  5. Susumu Kuroyanagi
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  7. Jun Yang
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  8. Kai Wang
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Correspondence to Zheng Jiang.

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Jiang, Z., Yamauchi, K., Yoshioka, K. et al. Support Vector Machine-Based Feature Selection for Classification of Liver Fibrosis Grade in Chronic Hepatitis C. J Med Syst 30, 389–394 (2006). https://doi.org/10.1007/s10916-006-9023-2

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  • DOI: https://doi.org/10.1007/s10916-006-9023-2

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