Diagnostic Method of Diabetes Based on Support Vector Machine and Tongue Images

Abstract

Objective. The purpose of this research is to develop a diagnostic method of diabetes based on standardized tongue image using support vector machine (SVM). Methods. Tongue images of 296 diabetic subjects and 531 nondiabetic subjects were collected by the TDA-1 digital tongue instrument. Tongue body and tongue coating were separated by the division-merging method and chrominance-threshold method. With extracted color and texture features of the tongue image as input variables, the diagnostic model of diabetes with SVM was trained. After optimizing the combination of SVM kernel parameters and input variables, the influences of the combinations on the model were analyzed. Results. After normalizing parameters of tongue images, the accuracy rate of diabetes predication was increased from 77.83% to 78.77%. The accuracy rate and area under curve (AUC) were not reduced after reducing the dimensions of tongue features with principal component analysis (PCA), while substantially saving the training time. During the training for selecting SVM parameters by genetic algorithm (GA), the accuracy rate of cross-validation was grown from 72% or so to 83.06%. Finally, we compare with several state-of-the-art algorithms, and experimental results show that our algorithm has the best predictive accuracy. Conclusions. The diagnostic method of diabetes on the basis of tongue images in Traditional Chinese Medicine (TCM) is of great value, indicating the feasibility of digitalized tongue diagnosis.

Prediction of fasting plasma glucose and glycated haemoglobin using machine learning based on tongue features

“…The current research is mainly to use statistical methods to explore the relationship between tongue features and diabetes and pre-diabetes. Given the tongue features, the diabetes risk prediction model established by machine learning method mainly performs qualitative classi cation prediction [11,12]. In our study, the prediction accuracy of the model is very high, MSE of FPG and HbA 1c prediction was 0.601 and 0.272 respectively, and the interpretability of the model is also very strong.…”

“…As a non-invasive and readily available feature, purple tongue, thick tongue coating, and yellow tongue coating can be used for early screening of diabetes [10]. At present, the researches that use machine learning to analyze the features of tongue image and establish a diabetes risk prediction model are mostly qualitative and two-category studies [11,12].…”

Human Tongue Thermography Could Be a Prognostic Tool for Prescreening the Type II Diabetes Mellitus

“…Zhang et al extracted the color and texture features from the digital tongue images from diabetic and nondiabetic subjects to develop the diagnostic technique for DM. ey observed the SVM classifier has comparatively achieved higher accuracy rate (79.72%) than k-NN (78.77%), NB (75.94%), and backpropagation neural network (75.00%) [66].…”

Deep Convolutional Neural Networks for Classifying Body Constitution Based on Face Image

“…There are many algorithms in pattern classification, such as Naive Bayes classifier [41], support vector machine classifier [42], Random Forest classifier [43], KNN classifier [44], Decision Tree classifier [45], Logistic Regression classifier [46], and Gradient Descent Boosting classifier [47]. By extracting the features in Section 2.2 and then entering the different classifiers, different classification results are obtained.…”