Diagnostic Method of Diabetes Based on Support Vector Machine and Tongue Images.
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...
| Publicado en: | BioMed Research International Vol. 2017; pp. 1 - 10 |
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| Autores principales: | , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
1/4/2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=120551150&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 120551150 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 1/4/2017 vid: 2017 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 120551150 120551150 120551150 10.1155/2017/7961494 120551150 ppf: 1 ppct: 9 formats: fmt: @attributes: type: P tig: atl: Diagnostic Method of Diabetes Based on Support Vector Machine and Tongue Images. aug: au: Zhang, Jianfeng Xu, Jiatuo Hu, Xiaojuan Chen, Qingguang Tu, Liping Huang, Jingbin Cui, Ji affil: Basic Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China sug: subj: Diabetes Mellitus Diagnosis Technology, Medical Human Tongue Anatomy and Histology Body Image Methods Consent Patient Selection World Health Organization Biomechanics Color ROC Curve Sensitivity and Specificity Male Female Funding Source Male Female ab: 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. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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