Research Article Identifying Potential Clinical Syndromes of Hepatocellular Carcinoma Using PSO-Based Hierarchical Feature Selection Algorithm Zhiwei Ji 1 and Bing Wang 1,2,3 1 School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China 2 he Advanced Research Institute of Intelligent Sensing Network, Tongji University, Shanghai 201804, China 3 he Key Laboratory of Embedded System and Service Computing, Tongji University, Ministry of Education, Shanghai 201804, China Correspondence should be addressed to Bing Wang; wangbing@ustc.edu Received 17 December 2013; Revised 7 February 2014; Accepted 10 February 2014; Published 17 March 2014 Academic Editor: Jose C. Nacher Copyright © 2014 Z. Ji and B. Wang. his 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. Hepatocellular carcinoma (HCC) is one of the most common malignant tumors. Clinical symptoms attributable to HCC are usually absent, thus oten 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. he 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. he 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. he results show that our computational model can facilitate the clinical diagnosis of HCC. 1. Introduction Hepatocellular carcinoma (HCC) is the third most common cause of cancer-related death worldwide and the leading cause of death in patients with cirrhosis [1, 2]. In clinical practice, symptoms attributable to HCC are usually absent, so the majority of patients are diagnosed with advanced disease, oten precluding potentially curative therapies. his has resulted, in part, in a 5-year overall survival rate of 12% and a median survival following diagnosis ranging from 6 to 20 months [3, 4]. herefore, timely and accurate diagnosis is very important for treatment of HCC. Currently, the modalities employed in the diagnosis of HCC mainly include cross- sectional imaging, biopsy, and serum AFP, which depend on both the size of the lesion and underlying liver function, and some of them are controversial [5, 6]. Traditional Chinese Medicine (TCM) is one of the most popular complementary and alternative medicine modalities. It plays an active role in diagnosis and treatment of HCC in Chinese and East some Asian countries [7, 8]. Diferent from other diagnostic methods, it is possible to accurately diagnose HCC using inspection, auscultation and olfaction, inquiry, and pulse taking and palpation [8]. In this study, we will work on a TCM clinical dataset, which is observed from 120 HCC patients. Each patient is observed on 147 clinical symptoms and a positive score is evaluated to indicate total positive strength of symptoms. Based on this TCM dataset, we could achieve two aims: (1) screening the potential clinical syndromes for this cancer and (2) inferring the relationships among the potential clinical features via Bayesian network analysis. However, the computational cost will be exceedingly high if the dimensions of the raw dataset Hindawi Publishing Corporation BioMed Research International Volume 2014, Article ID 127572, 12 pages http://dx.doi.org/10.1155/2014/127572