Study of TCM Syndrome Identification Modes for Patients with Type 2 Diabetes Mellitus Based on Data Mining

Tieniu Zhao; Xiaonan Yang; Ruixin Wan; Lihui Yan; Rongrong Yang; Yuanyuan Guan; Dongjun Wang; Huijun Wang; Hongwu Wang

doi:10.1155/2021/5528550

Study of TCM Syndrome Identification Modes for Patients with Type 2 Diabetes Mellitus Based on Data Mining

Evid Based Complement Alternat Med. 2021 Sep 6:2021:5528550. doi: 10.1155/2021/5528550. eCollection 2021.

Authors

Tieniu Zhao¹, Xiaonan Yang², Ruixin Wan³, Lihui Yan⁴, Rongrong Yang⁵, Yuanyuan Guan⁶, Dongjun Wang⁶, Huijun Wang⁷, Hongwu Wang¹

Affiliations

¹ School of Health Science and Engineering, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China.
² Department of Internal Medicine, Tianjin Hongqiao District Hospital of Traditional Chinese Medicine, Tianjin 300131, China.
³ Technology and Culture Exchange Center, China Soong Ching Ling Youth Science, Beijing 100080, China.
⁴ NHC Key Laboratoryo f Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin 300134, China.
⁵ Department of Public Health, School of Health Science and Engineering, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China.
⁶ Graduate School, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China.
⁷ Department of Typhoid, School of Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China.

Abstract

Objective: To establish the diagnosis model for syndromes of type 2 diabetes mellitus (T2-DM) and explore symptoms, the pulse and tongue signs, and laboratory indexes related to syndromes of T2-DM.

Methods: A syndromatologic and laboratory investigation was conducted in 554 T2-DM patients with 58 symptoms, 14 tongue signs, 6 pulse signs, and 12 laboratory indexes. The clinical data on the syndrome were collected and analyzed by using logistic regression analysis, decision tree, and K-nearest neighbor to establish a diagnostic model for effectively distinguishing the typical syndromes in T2-DM patients.

Results: The most typical syndromes revealed in T2-DM were stomach heat flourishing (SHF) syndrome (261 patients, accounting for 47.1%) and Qi-Yin deficiency (QYD) syndrome (293 patients, 52.9%). According to the clinical data of the patients with these two syndromes, variables including 6 symptoms and signs, 2 pulse signs, 1 tongue sign, and 2 laboratory indicators were introduced into the logistic regression model. All of them were statistically significant. Then, a diagnostic model constructed by QUEST and CHAID algorithms of the decision tree for identifying the two syndromes was proved to have an accurate diagnostic rate of 85.2%. It was found that the following sign and symptoms were effective to differentiate these two syndromes: odor in the mouth, polyphagia, vulnerability to starvation, burning sensation in the stomach, fatigue, limb weakness, slippery and replete pulse, weak pulse, pink tongue, oral glucose tolerance test, and hemoglobin A1C. A classification model constructed by the K-nearest neighbor method to identify the two syndromes showed an accurate diagnostic rate of 88.3%. Three major statistically significant predictors included in the model were slippery and replete pulse, polyphagia, and weak pulse (P < 0.05).

Conclusion: A model for distinguishing the two typical syndromes (SHF syndrome and QYD syndrome) in T2-DM patients was effectively established. This model could help to provide methodological support for the standardization of traditional Chinese medicine (TCM) syndrome differentiation methods.