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

Authors:
Zheng Jiang;Kazunobu Yamauchi;Kentaro Yoshioka;Kazuma Aoki;Susumu Kuroyanagi;Akira Iwata;Jun Yang;Kai Wang
Affiliations:
Department of Medical Information and Management Science, Graduate School of Medicine, Nagoya University, Nagoya, Japan 466-8550;Department of Medical Information and Management Science, Graduate School of Medicine, Nagoya University, Nagoya, Japan 466-8550;Department of Hepato-Gastroenterology, Graduate School of Medicine, Nagoya University, Nagoya, Japan 466-8550;Iwata-Lab Department of Electrical and Computer Engineering, Nagoya Institute of Technology, Nagoya, Japan;Iwata-Lab Department of Electrical and Computer Engineering, Nagoya Institute of Technology, Nagoya, Japan;Iwata-Lab Department of Electrical and Computer Engineering, Nagoya Institute of Technology, Nagoya, Japan;Department of Medical Information and Management Science, Graduate School of Medicine, Nagoya University, Nagoya, Japan 466-8550;Department of Medical Information and Management Science, Graduate School of Medicine, Nagoya University, Nagoya, Japan 466-8550
Venue:
Journal of Medical Systems
Year:
2006

Citing 4
Cited 4

Floating search methods in feature selection

Pattern Recognition Letters
The nature of statistical learning theory

The nature of statistical learning theory
Support-Vector Networks

Machine Learning
Use of Support Vector Machines and Neural Network in Diagnosis of Neuromuscular Disorders

Journal of Medical Systems

Sequential multi-criteria feature selection algorithm based on agent genetic algorithm

Applied Intelligence
Evolutionary-driven support vector machines for determining the degree of liver fibrosis in chronic hepatitis C

Artificial Intelligence in Medicine
Feature selection for a cooperative coevolutionary classifier in liver fibrosis diagnosis

Computers in Biology and Medicine
Hepatitis disease diagnosis using a novel hybrid method based on support vector machine and simulated annealing (SVM-SA)

Computer Methods and Programs in Biomedicine

Quantified Score

Hi-index	0.00

Visualization

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.