Zheng Classification with Missing Feature Values Using Local-Validity Approach

Yan Wang,Lizhuang Ma

doi:10.1155/2013/493626

Abstract

Zheng classification is a very important step in the diagnosis of traditional Chinese medicine (TCM). In clinical practice of TCM, feature values are often missing and incomplete cases. The performance of Zheng classification is strictly related to rates of missing feature values. Based on the pattern of the missing feature values, a new approach named local-validity is proposed to classify zheng classification with missing feature values. Firstly, the maximum submatrix for the given dataset is constructed and local-validity method finds subsets of cases for which all of the feature values are available. To reduce the computational scale and improve the classification accuracy, the method clusters subsets with similar patterns to form local-validity subsets. Finally, the proposed method trains a classifier for each local-validity subset and combines the outputs of individual classifiers to diagnose zheng classification. The proposed method is applied to the real liver cirrhosis dataset and three public datasets. Experimental results show that classification performance of local-validity method is superior to the widely used methods under missing feature values.

Highlights

To evaluate the performance of the proposed method, we carried out experiments on a real Traditional Chinese medicine (TCM) liver cirrhosis dataset with missing data
Various machine-learning algorithms have been used to construct the zheng classification model in TCM, most of them deal with complete feature values
Missing feature values are inevitable in TCM clinical application

Summary

Introduction

Traditional Chinese medicine (TCM) is one of the most important complementary medicines used increasingly in the world [1]. Zheng classification enables the doctor to determine the stage that the disease developed and the location of the disease [2]. Zheng classification is the method of recognizing and diagnosing diseases by analyzing patient information based on TCM theories and the doctor’s experiences [3]. In an attempt to achieve effective and objective standard of Zheng classification, various data mining approaches are used to construct the classifier on TCM dataset.

Methods

Results

Conclusion