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

Abstract

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.

A systematic review of multi-label feature selection and a new method based on label construction

“…This behavior could be partly explained by the relative lower computational cost in comparison with other alternatives. In addition, these strategies can be combined, as exemplified by IG-BR and some proposals from the related work [34][35][36][37]5]. In particular, these filter methods apply the Information Gain importance measure in binary data directly or indirectly transformed by the BR approach, a first-order strategy.…”

Research on Zheng Classification Fusing Pulse Parameters in Coronary Heart Disease

“…The task of multi-label learning (or multi-label classification) is to learn a function h : χ → 2 y that maps each instance x ∈ χ into a set of proper labels h ( x )⊆ y [25]. REAL, a new multi-label leaning algorithm with features selected through the maximization of mutual information, was proposed by our research team and was confirmed suitable for TCM Zheng differentiation [13, 26]. For this method, feature variables associated mostly with Zheng were selected according to maximization of mutual information.…”

“…Therefore, TCM Zheng patterns can be classified as a multi-label problem that traditional single-label learning algorithms cannot solve. The multi-label learning (MLL) algorithm REAL is suitable for solving multi-label recognition of TCM Zheng patterns [13]. This algorithm was applied to construct Zheng classification models based on different datasets.…”

Multi‐label feature selection with application to TCM state identification

“…TCM state identification is researched for the relationships of symptoms, syndromes and locations and natures of diseases, which is complex and ambiguous. TCM state identification can be applied in the study of various diseases in Western Medicine, such as coronary heart disease (CHD), Chronic gastritis (CG), and lupus erythematosus . TCM state identification expounds the objective and obscure relationships of symptoms, syndromes, and locations and natures of diseases by considering all things as a whole and correlating with each other.…”