A New Method for Syndrome Classification of Non-Small-Cell Lung Cancer Based on Data of Tongue and Pulse with Machine Learning. (11th August 2021)

• Record Type: Journal Article
• Authors: 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
• Other Names: Zhang Yue Academic Editor.
• Is Part Of: BioMed research international. Volume 2021(2021)
• Publication Date: 2021-08-11
• Subjects: Medicine -- Periodicals; Biology -- Periodicals; Biotechnology -- Periodicals; Life sciences -- Periodicals; 610.5
• DOI: 10.1155/2021/1337558
• Languages: English
• ISSNs: 2314-6133

Abstract

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 t4 and t5 . 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.