Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective

Abstract

As a complementary and alternative medicine in medical field, traditional Chinese medicine (TCM) has drawn great attention in the domestic field and overseas. In practice, TCM provides a quite distinct methodology to patient diagnosis and treatment compared to western medicine (WM). Syndrome (ZHENG or pattern) is differentiated by a set of symptoms and signs examined from an individual by four main diagnostic methods: inspection, auscultation and olfaction, interrogation, and palpation which reflects the pathological and physiological changes of disease occurrence and development. Patient classification is to divide patients into several classes based on different criteria. In this paper, from the machine learning perspective, a survey on patient classification issue will be summarized on three major aspects of TCM: sign classification, syndrome differentiation, and disease classification. With the consideration of different diagnostic data analyzed by different computational methods, we present the overview for four subfields of TCM diagnosis, respectively. For each subfield, we design a rectangular reference list with applications in the horizontal direction and machine learning algorithms in the longitudinal direction. According to the current development of objective TCM diagnosis for patient classification, a discussion of the research issues around machine learning techniques with applications to TCM diagnosis is given to facilitate the further research for TCM patient classification.

Diagnostic Method of Diabetes Based on Support Vector Machine and Tongue Images

“…Based on the results above, various machine learning methods have been used in tongue manifestation recognition or classification, such as support vector machine (SVM) [9–11], k Nearest Neighbor ( k -NN) [12, 13], Naive Bayes [11], Decision Tree [11], and Neural Network [9, 14]. Throughout all mentioned works on the inspection, the popular machine learning algorithms, such as k -NN and SVM, are still the first choice in current literature [15]. Though the identification and classification of tongue image have made certain achievements in the past researches, there still existed some issues, firstly, a standard lighting source environment is needed in the tongue image collection, and an effective method is necessary in the tongue image analysis.…”

The Classification of Tongue Colors with Standardized Acquisition and ICC Profile Correction in Traditional Chinese Medicine

“…Dunnett's T3 multiple comparison test in ANOVA would be used for the pairwise comparison of L ∗ , a ∗ , and b ∗ value of different color groups in SPSS18.0. Consider the idea that SVM is the most commonly used supervised machine learning method in tongue diagnosis [31], while Random forest is rarely used in this area according to the reports; besides the tongue color classification in this study is a multiclassification research; meanwhile the samples are limited and imbalanced, and the feature number for the studied images is relatively small; in order to obtain a better classification accuracy, we will use SVM and Random forest in the modeling and analytical test for the experimental data in WEKA software [32]. …”

A multimodal deep architecture for traditional Chinese medicine diagnosis

“…With the increasingly popularity of TCM, related researches of computer‐aided diagnosis have also been proposed. They mainly focus on one of the TCM diagnostic methods, for example, tongue inspection diagnosis, 21 facial inspection diagnosis, 22 auscultation, 23 olfaction diagnosis, 24 inquiry, 25 and palpation 26 . Most of the researches use traditional machine learning methods and feature‐engineering tricks.…”