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Relationships Between Diurnal Changes of Tongue Coating Microbiota and Intestinal Microbiota
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In: Front Cell Infect Microbiol (2022)
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A New Method for Syndrome Classification of Non-Small-Cell Lung Cancer Based on Data of Tongue and Pulse with Machine Learning
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Shi, Yu-lin; Liu, Jia-yi; Hu, Xiao-juan; Tu, Li-ping; Cui, Ji; Li, Jun; Bi, Zi-juan; Li, Jia-cai; Xu, Ling; Xu, Jia-tuo
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In: Biomed Res Int (2021)
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Abstract:
OBJECTIVE: To explore the data characteristics of tongue and pulse of non-small-cell lung cancer with Qi deficiency syndrome and Yin deficiency syndrome, establish syndrome classification model based on data of tongue and pulse by using machine learning methods, and evaluate the feasibility of syndrome classification based on data of tongue and pulse. METHODS: We collected tongue and pulse of non-small-cell lung cancer patients with Qi deficiency syndrome (n = 163), patients with Yin deficiency syndrome (n = 174), and healthy controls (n = 185) using intelligent tongue diagnosis analysis instrument and pulse diagnosis analysis instrument, respectively. We described the characteristics and examined the correlation of data of tongue and pulse. Four machine learning methods, namely, random forest, logistic regression, support vector machine, and neural network, were used to establish the classification models based on symptom, tongue and pulse, and symptom and tongue and pulse, respectively. RESULTS: Significant difference indices of tongue diagnosis between Qi deficiency syndrome and Yin deficiency syndrome were TB-a, TB-S, TB-Cr, TC-a, TC-S, TC-Cr, perAll, and the tongue coating texture indices including TC-CON, TC-ASM, TC-MEAN, and TC-ENT. Significant difference indices of pulse diagnosis were t(4) and t(5). The classification performance of each model based on different datasets was as follows: tongue and pulse < symptom < symptom and tongue and pulse. The neural network model had a better classification performance for symptom and tongue and pulse datasets, with an area under the ROC curves and accuracy rate which were 0.9401 and 0.8806. CONCLUSIONS: It was feasible to use tongue data and pulse data as one of the objective diagnostic basis in Qi deficiency syndrome and Yin deficiency syndrome of non-small-cell lung cancer.
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Keyword:
Research Article
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URL: https://doi.org/10.1155/2021/1337558
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8373490/
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Tongue image quality assessment based on a deep convolutional neural network
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In: BMC Med Inform Decis Mak (2021)
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Teaching Standards, Approaches, and Techniques for K-12 Chinese Classes in the US
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In: Chinese Language Teaching Methodology and Technology (2020)
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A Clustering Framework for Lexical Normalization of Roman Urdu ...
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Diversity by Phonetics and its Application in Neural Machine Translation ...
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The Classification of Tongue Colors with Standardized Acquisition and ICC Profile Correction in Traditional Chinese Medicine
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