文献类型：期刊文献

英文题名：Application of Multilabel Learning Using the Relevant Feature for Each Label in Chronic Gastritis Syndrome Diagnosis

作者：Liu, Guo-Ping[2];Yan, Jian-Jun[1];Wang, Yi-Qin[2];Fu, Jing-Jing[2];Xu, Zhao-Xia[2];Guo, Rui[2];Qian, Peng[2]

机构：[1]E China Univ Sci & Technol, Ctr Mechatron Engn, Shanghai 200237, Peoples R China;[2]Shanghai Univ Tradit Chinese Med, Basic Med Coll, Lab Informat Access & Synth TCM Diag 4, Shanghai 201203, Peoples R China

年份：2012

卷号：2012

外文期刊名：EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000305631700001)】；

基金：This work was supported by the National Natural Science Foundation of China (Grant no. 30901897 and 81173199), the Shanghai 3th Leading Academic Discipline Project (Grant no. S30302), and the National Natural Science Foundation of China (Grant no. 30701072.)

语种：英文

摘要：Background. In Traditional Chinese Medicine (TCM), most of the algorithms are used to solve problems of syndrome diagnosis that only focus on one syndrome, that is, single label learning. However, in clinical practice, patients may simultaneously have more than one syndrome, which has its own symptoms (signs). Methods. We employed a multilabel learning using the relevant feature for each label (REAL) algorithm to construct a syndrome diagnostic model for chronic gastritis (CG) in TCM. REAL combines feature selection methods to select the significant symptoms (signs) of CG. The method was tested on 919 patients using the standard scale. Results. The highest prediction accuracy was achieved when 20 features were selected. The features selected with the information gain were more consistent with the TCM theory. The lowest average accuracy was 54% using multi-label neural networks (BP-MLL), whereas the highest was 82% using REAL for constructing the diagnostic model. For coverage, hamming loss, and ranking loss, the values obtained using the REAL algorithm were the lowest at 0.160, 0.142, and 0.177, respectively. Conclusion. REAL extracts the relevant symptoms (signs) for each syndrome and improves its recognition accuracy. Moreover, the studies will provide a reference for constructing syndrome diagnostic models and guide clinical practice.