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| Analysis on the law of tongue and pulse manifestation and syndrome types of clinical diabetes mellitus based on data mining |
| GUAN Yuanyuan1 TIAN Chunying1 SUN Xuan1 WANG Dongjun1 ZHU Qingqing1 ZHOU Zonghui1 WANG Hongwu2 |
1.Graduate School, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China;
2.School of Health Science and Engineering, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China |
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Abstract Objective To study the distribution and correlation of tongue, pulse manifestation and syndrome types in clinical diabetes research. Methods CNKI, VIP and Wanfang Data were collected to study the clinical journals, conferences and dissertations on tongue, pulse manifestation and syndrome types of diabetes mellitus from the establishment of the database to December 2019. The search strategy was “diabetes” and “tongue manifestation” “pulse manifestation” “syndrome type”. The database of diabetes tongue and pulse manifestation and syndrome types was established, and descriptive statistics, association rule analysis and cluster analysis on the data were performed. Results The tongue and pulse manifestation and syndrome types with the highest frequency in the clinical study of diabetes were dark red tongue, greasy fur, deep pulse, stringy pulse, syndrome of endogenous heat due to yin deficiency. The most relevant combination of tongue and pulse manifestation were red tongue-deep pulse + stringy pulse, greasy fur-deep pulse + stringy pulse, greasy fur-slippery pulse + stringy pulse. In the results of the fourth-order association rules, the first four items with the highest degree were tongue and pulse transformations on the basis of syndrome of deficiency of both qi and yin and syndrome of endogenous heat due to yin deficiency, which were slippery pulse + stringy pulse + dark red tongue, slippery pulse + dark red tongue + greasy fur, plump tongue + deep pulse + red tongue, slippery pulse + dark red tongue + yellow fur. Conclusion The transfomation of tongue and pulse manifestation and syndrome types in clinical diabetes mellitus is complicated. The application of association rules in data mining analysis can provide theoretical guidance for clinical diagnosis and treatment of treating diabetes mellitus with traditional Chinese medicine.
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