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BioMed Research International
Volume 2014, Article ID 127572, 12 pages
http://dx.doi.org/10.1155/2014/127572
Research Article

Identifying Potential Clinical Syndromes of Hepatocellular Carcinoma Using PSO-Based Hierarchical Feature Selection Algorithm

Zhiwei Ji1 and Bing Wang1,2,3

1School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China
2The Advanced Research Institute of Intelligent Sensing Network, Tongji University, Shanghai 201804, China
3The Key Laboratory of Embedded System and Service Computing, Tongji University, Ministry of Education, Shanghai 201804, China

Received 17 December 2013; Revised 7 February 2014; Accepted 10 February 2014; Published 17 March 2014

Academic Editor: Jose C. Nacher

Copyright © 2014 Zhiwei Ji and Bing Wang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors. Clinical symptoms attributable to HCC are usually absent, thus often miss the best therapeutic opportunities. Traditional Chinese Medicine (TCM) plays an active role in diagnosis and treatment of HCC. In this paper, we proposed a particle swarm optimization-based hierarchical feature selection (PSOHFS) model to infer potential syndromes for diagnosis of HCC. Firstly, the hierarchical feature representation is developed by a three-layer tree. The clinical symptoms and positive score of patient are leaf nodes and root in the tree, respectively, while each syndrome feature on the middle layer is extracted from a group of symptoms. Secondly, an improved PSO-based algorithm is applied in a new reduced feature space to search an optimal syndrome subset. Based on the result of feature selection, the causal relationships of symptoms and syndromes are inferred via Bayesian networks. In our experiment, 147 symptoms were aggregated into 27 groups and 27 syndrome features were extracted. The proposed approach discovered 24 syndromes which obviously improved the diagnosis accuracy. Finally, the Bayesian approach was applied to represent the causal relationships both at symptom and syndrome levels. The results show that our computational model can facilitate the clinical diagnosis of HCC.