Support vector machine-based feature selection for classification of liver fibrosis grade in chronic hepatitis C.
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.
Jiang
Zheng
Z
Department of Medical Information and Management Science, Graduate School of Medicine, Nagoya University, 65, Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan. jianzhen@med.nagoya-u.ac.jp
Yamauchi
Kazunobu
K
Yoshioka
Kentaro
K
Aoki
Kazuma
K
Kuroyanagi
Susumu
S
Iwata
Akira
A
Yang
Jun
J
Wang
Kai
K
D000328:Adult; D000465:Algorithms; D015415:Biomarkers; D019937:Diagnostic Techniques and Procedures; D005260:Female; D019698:Hepatitis C, Chronic; D006801:Humans; D007564:Japan; D008103:Liver Cirrhosis / Q000097:blood / Q000145:classification*; D008297:Male; D008875:Middle Aged; D012189:Retrospective Studies
D015415:Biomarkers