NMFBFS: A NMF-Based Feature Selection Method in Identifying Pivotal Clinical Symptoms of Hepatocellular Carcinoma
Joint Authors
Ji, Zhiwei
Wang, Bing
Huang, De-Shuang
Meng, Guanmin
Yue, Xiaoqiang
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-10-12
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
Background.
Hepatocellular carcinoma (HCC) is a highly aggressive malignancy.
Traditional Chinese Medicine (TCM), with the characteristics of syndrome differentiation, plays an important role in the comprehensive treatment of HCC.
This study aims to develop a nonnegative matrix factorization- (NMF-) based feature selection approach (NMFBFS) to identify potential clinical symptoms for HCC patient stratification.
Methods.
The NMFBFS approach consisted of three major steps.
Firstly, statistics-based preliminary feature screening was designed to detect and remove irrelevant symptoms.
Secondly, NMF was employed to infer redundant symptoms.
Based on NMF-derived basis matrix, we defined a novel similarity measurement of intersymptoms.
Finally, we converted each group of redundant symptoms to a new single feature so that the dimension was further reduced.
Results.
Based on a clinical dataset consisting of 407 patient samples of HCC with 57 symptoms, NMFBFS approach detected 8 irrelevant symptoms and then identified 16 redundant symptoms within 6 groups.
Finally, an optimal feature subset with 39 clinical features was generated after compressing the redundant symptoms by groups.
The validation of classification performance shows that these 39 features obviously improve the prediction accuracy of HCC patients.
Conclusions.
Compared with other methods, NMFBFS has obvious advantages in identifying important clinical features of HCC.
American Psychological Association (APA)
Ji, Zhiwei& Meng, Guanmin& Huang, De-Shuang& Yue, Xiaoqiang& Wang, Bing. 2015. NMFBFS: A NMF-Based Feature Selection Method in Identifying Pivotal Clinical Symptoms of Hepatocellular Carcinoma. Computational and Mathematical Methods in Medicine،Vol. 2015, no. 2015, pp.1-12.
https://search.emarefa.net/detail/BIM-1058017
Modern Language Association (MLA)
Ji, Zhiwei…[et al.]. NMFBFS: A NMF-Based Feature Selection Method in Identifying Pivotal Clinical Symptoms of Hepatocellular Carcinoma. Computational and Mathematical Methods in Medicine No. 2015 (2015), pp.1-12.
https://search.emarefa.net/detail/BIM-1058017
American Medical Association (AMA)
Ji, Zhiwei& Meng, Guanmin& Huang, De-Shuang& Yue, Xiaoqiang& Wang, Bing. NMFBFS: A NMF-Based Feature Selection Method in Identifying Pivotal Clinical Symptoms of Hepatocellular Carcinoma. Computational and Mathematical Methods in Medicine. 2015. Vol. 2015, no. 2015, pp.1-12.
https://search.emarefa.net/detail/BIM-1058017
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1058017