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Intelligent decision support model using tongue image features for healthcare monitoring of diabetes diagnosis and classification

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Abstract

Diabetes Mellitus (DM) is a serious health problem that affects majority of peoples worldwide, the conventional diagnosis procedure estimates the amount of glucose level in blood and the treatment is to regulate the blood glucose to desired level. As an alternative, an ancient therapy that has been followed for more than two millennium period is the Traditional Chinese Medicine (TCM). Tongue feature analysis is the main strategy followed by the TCM experts as the diagnosing procedure to identify and locate diabetes. In this research article, a computer aided intelligent decision support model is developed, the CNN Dense Net framework is employed to identify the necessary features of the tongue image such as its color, texture, the fur coating, tooth markings, and the red spots. To perform classification Support Vector Machines (SVM) is employed to enhance its performance the SVM parameters are tuned by Particle Swarm Optimization (PSO) technique. The model is validated by the real-time dataset and the performance is compared with state of art methods to establish its efficiency and effectiveness. Simulation is carried out in MATLAB environment and evaluated in terms of performance metrics such as Accuracy, Sensitivity, and Specificity, Precision, F1 Score and Error rate. The proposed model attained an accuracy of 97.82%, precision of 98.18%, sensitivity of 98.44%, and specificity of 96.68%, F1 Score of 98.31% and Error rate of 0.02185 proving its superiority over previous approaches. The developed intelligent decision support model has proven its efficacy over the state-of-the-art techniques for diagnosing and classifying diabetes mellitus. The proposed approach ensures a better healthcare monitoring model for identifying diabetes mellitus and performs treatment to the humans at an early stage.

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References

  • Arunadevi B, Deepa SN (2013) Brain tumor tissue categorization in 3D magnetic resonance images using improved PSO for extreme learning machine. Prog Electromagnetics Res 49:31–54

    Article  Google Scholar 

  • Bai FX, Huang YH (2020) Research progress of TCM tongue diagnosis in spleen and stomach disease. Psychosomatic Med Res 2(1):19–24

    Google Scholar 

  • Buditjahjanto IGP, Rochmawati N, Peni RH (2017) Classification of lung disease syndromes in traditional Chinese medicine based on learning vector quantization. Adv Sci Lett 23(12):11879–11883

    Article  Google Scholar 

  • Cao M, Miao J, Wang L, Hai-Zhao L, Cui HT, Yu-Hong B (2020) The advances of traditional Chinese medicine in the treatment of liver diseases in 2019. Traditional Med Res 5(4):261

    Article  Google Scholar 

  • Deepa SN, Arunadevi B (2013) Extreme learning machine for classification of brain tumor in 3D MR images. Informatologia 46(2):111

    Google Scholar 

  • Fan S, Chen B, Zhang X, Hu X, Bao L, Yang X, Liu Z, Yu Y (2021) Machine learning algorithms in classifying TCM tongue features in diabetes mellitus and symptoms of gastric disease. Eur J Integrative Med 43:101288

    Article  Google Scholar 

  • He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  • Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700–4708

  • Huo CM, Zheng H, Su HY, Sun ZL, Cai YJ, Xu YF (2017) Tongue shape classification integrating image preprocessing and Convolution Neural Network. In 2017 2nd Asia-Pacific Conference on Intelligent Robot Systems (ACIRS), IEEE. pp 42–46

  • Joshi TN, Chawan PPM (2018) Diabetes prediction using machine learning techniques. Ijera 8(1):9–13

    Google Scholar 

  • Kennedy J, Eberhart R (1995) Particle swarm optimization. In Proceedings of ICNN'95-international conference on neural networks , IEEE, vol 4, pp 1942–1948

  • Li H, Zhang X, Hu G, Sun Y (2013) The improvement of the tooth-marked recognition method on the tongue images. In 2013 ICME International Conference on Complex Medical Engineering, IEEE, pp 412–415

  • Li X, Zhang Y, Cui Q, Yi X, Zhang Y (2018) Tooth-marked tongue recognition using multiple instance learning and cnn features. IEEE Trans Cybernetics 49(2):380–387

    Article  Google Scholar 

  • Li J, Yuan P, Hu X, Huang J, Cui L, Cui J, Ma X, Jiang T, Yao X, Li J, Shi Y (2021a) A tongue features fusion approach to predicting prediabetes and diabetes with machine learning. J Biomed Inform 115:103693

    Article  Google Scholar 

  • Li J, Chen Q, Hu X, Yuan P, Cui L, Tu L, Cui J, Huang J, Jiang T, Ma X, Yao X (2021b) Establishment of noninvasive diabetes risk prediction model based on tongue features and machine learning techniques. Int J Med Inform 149:104429

    Article  Google Scholar 

  • Lim WX, Chen Z, Ahmed A, Chandesa T, Liao I (2020) A review of machine learning techniques for applied eye fundus and tongue digital image processing with diabetes management system. arXiv:2012.15025

  • Lo LC, Cheng TL, Chen YJ, Natsagdorj S, Chiang JY (2015) TCM tongue diagnosis index of early-stage breast cancer. Complement Ther Med 23(5):705–713

    Article  Google Scholar 

  • Mu L, Sanders I (2010) Human tongue neuroanatomy: nerve supply and motor endplates. Clin Anat 23(7):777–791

    Article  Google Scholar 

  • Naveed S (2020) Early diabetes discovery from tongue images. Computer J. https://doi.org/10.1093/comjnl/bxaa022

    Article  Google Scholar 

  • Preshiya DJ (2015) Diagnosing diabetics with reflex zones of the tongue using colour image segmentation. J Chem Pharm Res 7(2):876–881

    Google Scholar 

  • Saeedi P, Petersohn I, Salpea P, Malanda B, Karuranga S, Unwin N, Colagiuri S, Guariguata L, Motala AA, Ogurtsova K, Shaw JE (2019) Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: results from the International Diabetes Federation Diabetes Atlas. Diabetes Res Clin Pract 157:107843

    Article  Google Scholar 

  • Sanders I, Mu L (2013) A three-dimensional atlas of human tongue muscles. Anat Rec 296(7):1102–1114

    Article  Google Scholar 

  • Sang X, Wang Z, Liu S, Wang R (2018) Relationship between traditional Chinese medicine (TCM) constitution and TCM syndrome in the diagnosis and treatment of chronic diseases. Chin Med Sci J 33(2):114–119

    Google Scholar 

  • Selvarani A, Suresh GR (2020) Decision support system for diabetes using tongue images. In 2020 International Conference on Communication and Signal Processing (ICCSP), IEEE, pp 0012–0016

  • Shao Q, Li X, Fu Z (2014). Recognition of teeth-marked tongue based on gradient of concave region. In 2014 International Conference on Audio, Language and Image Processing, IEEE, pp 968–972

  • Sivanantham E (2020) The steerable pyramid representation of tongue images to diagnose diabetic based on Gaussian mixture model. J Nat Remedies 21(7):1–3

    Google Scholar 

  • Srividhya E, Muthukumaravel A (2019) Diagnosis of diabetes by tongue analysis. In 2019 1st International Conference on Advances in Information Technology (ICAIT), IEEE, pp 217–222

  • Stone M, Woo J, Lee J, Poole T, Seagraves A, Chung M, Kim E, Murano EZ, Prince JL, Blemker SS (2018) Structure and variability in human tongue muscle anatomy. Comp Methods Biomechanics Biomed Eng Imaging Visualization 6(5):499–507

    Article  Google Scholar 

  • Suthaharan S (2016) Support vector machine. In: Machine learning models and algorithms for big data classification. Springer, Boston, MA, pp 207–235

  • Thirunavukkarasu U, Umapathy S, Krishnan PT, Janardanan K (2020) Human tongue thermography could be a prognostic tool for prescreening the type II diabetes mellitus. Evidence-Based Complementary Alternative Med 2020:1–16

    Article  Google Scholar 

  • Vijayalakshmi A, Shahaana M, Nivetha NCD, Subramaniam K (2020) Development of prognosis tool for type-II diabetics using tongue image analysis. In 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), IEEE, pp 617–619

  • Wang X, Zhang B, Yang Z, Wang H, Zhang D (2013) Statistical analysis of tongue images for feature extraction and diagnostics. IEEE Trans Image Process 22(12):5336–5347

    Article  Google Scholar 

  • Wang H, Zhang X, Cai Y (2014). Research on teeth marks recognition in tongue image. In 2014 International Conference on Medical Biometrics, IEEE, pp 80–84

  • Wang X, Liu J, Wu C, Liu J, Li Q, Chen Y, Wang X, Chen X, Pang X, Chang B, Lin J (2020) Artificial intelligence in tongue diagnosis: Using deep convolutional neural network for recognizing unhealthy tongue with tooth-mark. Comp Structural Biotechnol J 18:973–980

    Article  Google Scholar 

  • Wu L, Luo X, Xu Y (2020) Using convolutional neural network for diabetes mellitus diagnosis based on tongue images. J Engineering 2020(13):635–638

    Article  Google Scholar 

  • Xie J, Li Y, Wang N, Xin L, Fang Y, Liu J (2020) Feature selection and syndrome classification for rheumatoid arthritis patients with Traditional Chinese Medicine treatment. Eur J Integrative Med 34:101059

    Article  Google Scholar 

  • Xu Q, Zeng Y, Tang W, Peng W, Xia T, Li Z, Teng F, Li W, Guo J (2020) Multi-task joint learning model for segmenting and classifying tongue images using a deep neural network. IEEE J Biomed Health Inform 24(9):2481–2489

    Article  Google Scholar 

  • Yuan Y, Liao W (2020) Design and implementation of the Traditional Chinese medicine constitution system based on the diagnosis of tongue and consultation. IEEE Access 9:4266–4278

    Article  Google Scholar 

  • Zhang J, Xu J, Hu X, Chen Q, Tu L, Huang J, Cui J (2017) Diagnostic method of diabetes based on support vector machine and tongue images. BioMed Res Int 2017:1–9

    Google Scholar 

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Authors and Affiliations

  1. Department of Electrical Engineering, National Institute of Technology Arunachal Pradesh, Yupia, Arunachal Pradesh, 791112, India

    S. N. Deepa & Abhik Banerjee

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  1. S. N. Deepa
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  2. Abhik Banerjee
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Correspondence to S. N. Deepa.

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Deepa, S.N., Banerjee, A. Intelligent decision support model using tongue image features for healthcare monitoring of diabetes diagnosis and classification. Netw Model Anal Health Inform Bioinforma 10, 41 (2021). https://doi.org/10.1007/s13721-021-00319-1

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