A tongue features fusion approach to predicting prediabetes and diabetes with machine learning

Li, Jun; Yuan, Pei; Hu, Xiaojuan; Huang, Jingbin; Cui, Longtao; Cui, Ji; Ma, Xuxiang; Jiang, Tao; Yao, Xinghua; Li, Jiacai; Shi, Yulin; Bi, Zijuan; Wang, Yu; Fu, Haoran; Wang, Jue; Lin, Yen-Ting; Pai, Ching-Hsuan; Guo, Xiaojing; Zhou, Changle; Tu, Liping; Xu, Jiatuo

doi:10.1016/j.jbi.2021.103693

Cited by 61 publications

(37 citation statements)

References 36 publications

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“…The tongue indices were from three color spaces, Lab, HIS, and YCrCb (Qi et al, 2016;Sun et al, 2016;Schiller et al, 2018), each tongue and pulse index had its medical meaning (Qi et al, 2016;Luo et al, 2018;Li et al, 2021b;Shi et al, 2021). The indices of tongue diagnosis and pulse diagnosis and their corresponding clinical meaning are shown in Supplementary Table 1.…”

Section: Collecting Clinical Tongue Data and Pulse Datamentioning

confidence: 99%

A New Approach of Fatigue Classification Based on Data of Tongue and Pulse With Machine Learning

Shi

Yao

et al. 2022

Front. Physiol.

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BackgroundFatigue is a common and subjective symptom, which is associated with many diseases and suboptimal health status. A reliable and evidence-based approach is lacking to distinguish disease fatigue and non-disease fatigue. This study aimed to establish a method for early differential diagnosis of fatigue, which can be used to distinguish disease fatigue from non-disease fatigue, and to investigate the feasibility of characterizing fatigue states in a view of tongue and pulse data analysis.MethodsTongue and Face Diagnosis Analysis-1 (TFDA-1) instrument and Pulse Diagnosis Analysis-1 (PDA-1) instrument were used to collect tongue and pulse data. Four machine learning models were used to perform classification experiments of disease fatigue vs. non-disease fatigue.ResultsThe results showed that all the four classifiers over “Tongue & Pulse” joint data showed better performances than those only over tongue data or only over pulse data. The model accuracy rates based on logistic regression, support vector machine, random forest, and neural network were (85.51 ± 1.87)%, (83.78 ± 4.39)%, (83.27 ± 3.48)% and (85.82 ± 3.01)%, and with Area Under Curve estimates of 0.9160 ± 0.0136, 0.9106 ± 0.0365, 0.8959 ± 0.0254 and 0.9239 ± 0.0174, respectively.ConclusionThis study proposed and validated an innovative, non-invasive differential diagnosis approach. Results suggest that it is feasible to characterize disease fatigue and non-disease fatigue by using objective tongue data and pulse data.

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Section: Collecting Clinical Tongue Data and Pulse Datamentioning

confidence: 99%

A New Approach of Fatigue Classification Based on Data of Tongue and Pulse With Machine Learning

Shi

Yao

et al. 2022

Front. Physiol.

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“…Tongue indices can be divided into two categories: tongue body (TB) index and tongue coating (TC) index which mainly come from the three color spaces of Lab, HIS, and YCrCb [29][30][31][32]. Each parameter of tongue diagnosis and pulse diagnosis has its corresponding medical significance [32][33][34]. In tongue indices, they are R (Red), G (Green), B (Blue), H (Hue), S(Saturation), I (Intensity) and L (Light), a (red-green axis), b (yellow-blue axis), Y (brightness), Cr (difference between red signal and brightness), Cb (difference between blue signal and brightness), texture indices include CON (Contrast), ASM (Angular Second Moment), ENT (Entropy), MEAN (Mean), and tongue coating indices include perAll and perPart.…”

Section: Diagnosticmentioning

confidence: 99%

A New Method for Syndrome Classification of Non-Small-Cell Lung Cancer Based on Data of Tongue and Pulse with Machine Learning

Shi

Liu

et al. 2021

BioMed Research International

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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.

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“…Tongue image acquisition equipment (type TFDA-1 tongue diagnosis instrument) (Fig. 1) and analysis software (Tongue diagnosis analysis system, TDAS) were designed and developed by the intelligent diagnosis technology research team in Shanghai University of TCM [9] . TFDA-1 is equipped with a high-definition camera and standard light source to ensure a stable shooting environment.…”

Section: Collection and Analysis Of Tongue Featuresmentioning

confidence: 99%

The Study of Key Tongue Features for Prediabetes Risk and Diabetes Risk using Principal Component Analysis and Logistic Regression

et al. 2021

Preprint

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Background: Carrying out routine diabetes risk assessment and prediction is conducive to controlling the incidence of diabetes. Previous studies have confirmed that tongue features can reflect changes in glucose metabolism. We want to find the key tongue features related to glucose metabolism for early warning of prediabetes and diabetes. This will benefit the prevention and control of diabetes.Methods: We investigated 719 subjects in Shuguang Hospital affiliated with Shanghai University of TCM in Shanghai, China, during the August 2018 – December 2019 period. We used PCA to reconstruct 25 new features on the basis of the original features, and used logistic regression to analyze these new features. Based on the factor loading method, we associate the new features with the original features. Finally, we determined the key tongue features and change patterns related to prediabetes risk and diabetes risk.Results: For the prediabetes, TB-ASM, TC-ASM and TB-Cr are protective factors. TB-CON, TC-CON, TB-MEAN, TC-MEAN, TB-ENT and TC-ENT are risk factors. For the diabetes, TB-a, TB-S, TB-Cr, TB-b and TC-b are protective factors, and Per-all and TB-Cb are risk factors. Conclusion: The PCA eliminates the redundancy of original features and refines the original features. The PCA combined with logistic regression found the key tongue features reflecting glucose metabolism, and clarified the significance of the changes in these tongue features in the prediabetes and diabetes population. This study provides a research foundation for using tongue features to implement early warning of diabetes risk.

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A tongue features fusion approach to predicting prediabetes and diabetes with machine learning

Cited by 61 publications

References 36 publications

A New Approach of Fatigue Classification Based on Data of Tongue and Pulse With Machine Learning

A New Approach of Fatigue Classification Based on Data of Tongue and Pulse With Machine Learning

A New Method for Syndrome Classification of Non-Small-Cell Lung Cancer Based on Data of Tongue and Pulse with Machine Learning

The Study of Key Tongue Features for Prediabetes Risk and Diabetes Risk using Principal Component Analysis and Logistic Regression

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