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