Machine learning algorithms in classifying TCM tongue features in diabetes mellitus and symptoms of gastric disease

Abstract

IntroductionTongue diagnosis in Traditional Chinese Medicine (TCM) is used in clinical practice for disease diagnosis but this may be perceived as being too subjective. Objective technological approaches can lead to more accurate diagnosis and have become a focus for research. The aim of this study was to explore different tongue features in people with diabetes, gastric symptoms using images collected using digital tongue imaging. MethodsMachine learning algorithms were used to classify diabetes mellitus(DM) patients and healthy participants, and patients exhibiting two different TCM gastric disease symptoms (liver-stomach disharmony and spleen-stomach deficiency). Multi-type feature extraction and selection from 466 tongue images was conducted. In the feature extraction stage, texture features and four TCM tongue features were identified: constitution color, coating color, cracks, and plumpness and slenderness. In the classification stage, two different classification algorithms were employed, Random Forest and Support Vector Machine, to classify DM and TCM gastric disease symptoms. ResultsRegarding the classification results, the area under the receiver operating characteristic curve (AUC) reached 97.9% ± 0.1%, with maximum sensitivity and specificity of 93.5% ± 0.6% and 95.0% ± 0.8%, for DM patients and healthy volunteers respectively. For the classification of TCM gastric disease symptoms, the highest AUC was only 69.4% ± 2.6%, with a sensitivity and specificity of 65.2% ± 2.9% and 63.7% ± 2.1%, respectively. ConclusionsExperiments shows that the TCM tongue features (color, cracks, plumpness and slenderness) and texture features have good performance for disease diagnosis. However, classification of DM was significantly better than for TCM gastric symptoms. The RF classification model was also significantly better than the SVM classifier model.