Journal ArticleDOI
Support Vector Machine-Based Feature Selection for Classification of Liver Fibrosis Grade in Chronic Hepatitis C
Zheng Jiang,Kazunobu Yamauchi,Kentaro Yoshioka,Aoki Kazuma,Susumu Kuroyanagi,Akira Iwata,Jun Yang,Kai Wang +7 more
TLDR
This 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.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 biopsyread more
Citations
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Journal ArticleDOI
Hepatitis disease diagnosis using a novel hybrid method based on support vector machine and simulated annealing (SVM-SA)
TL;DR: A novel machine learning method that hybridizes support vector machine (SVM) and simulated annealing (SA) is proposed that is very promising with regard to the other classification methods in the literature for this problem.
Journal ArticleDOI
A critical assessment of feature selection methods for biomarker discovery in clinical proteomics
Christin Christin,Huub C. J. Hoefsloot,Huub C. J. Hoefsloot,Age K. Smilde,Age K. Smilde,Berend Hoekman,Frank Suits,Rainer Bischoff,Peter Horvatovich +8 more
TL;DR: It is concluded that the univariate t test and the mww test with multiple testing corrections are not applicable to data sets with small sample sizes, but their performance improves markedly with increasing sample size up to a point at which they outperform the other methods.
Journal ArticleDOI
Machine-learning-based classification of real-time tissue elastography for hepatic fibrosis in patients with chronic hepatitis B.
Yang Chen,Yang Chen,Yan Luo,Wei Huang,Die Hu,Rong-qin Zheng,Shu-zhen Cong,Fan-kun Meng,Hong Yang,Hong-jun Lin,Yan Sun,Xiu-yan Wang,Tao Wu,Jie Ren,Shu-Fang Pei,Ying Zheng,Yun He,Yu Hu,Na Yang,Hongmei Yan +19 more
TL;DR: The experimental results showed that the adopted classifiers significantly outperformed the LFI method and that the Random Forest(RF) classifier provided the highest average accuracy among the four machine algorithms.
Journal ArticleDOI
Particle swarm optimization for feature selection with application in obstructive sleep apnea diagnosis
TL;DR: It is concluded that the proposed analytical approach by integrating particle swarm optimization (PSO) and the 1-NN method can help identify important factors and provide a feasible model for diagnosing medical disease.
Journal ArticleDOI
Evolutionary-driven support vector machines for determining the degree of liver fibrosis in chronic hepatitis C
TL;DR: The use of the evolutionary technique for fibrosis degree prediction triggers simplicity and offers a direct expression of the influence of dynamically selected indicators on the corresponding stage, which significantly surpasses the classical support vector machines, which are both widely used and technically sound.
References
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TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Journal ArticleDOI
Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Statistical learning theory
TL;DR: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
Journal ArticleDOI
A simple noninvasive index can predict both significant fibrosis and cirrhosis in patients with chronic hepatitis C
Chun-Tao Wai,Joel K. Greenson,Robert J. Fontana,John D. Kalbfleisch,Jorge A. Marrero,Hari S. Conjeevaram,Anna S.F. Lok +6 more
TL;DR: It is shown that a simple index using readily available laboratory results can identify CHC patients with significant fibrosis and cirrhosis with a high degree of accuracy and may decrease the need for staging liver biopsy specimens among patients with CHC.