An automatic diabetes diagnosis system based on LDA-Wavelet Support Vector Machine Classifier - Calisir, Duygu; Dogantekin, Esin (Pergamon-Elsevier Science Ltd, 2011)
In this paper, an automatic diagnosis system for diabetes on Linear Discriminant Analysis (LDA) and Morlet Wavelet Support Vector Machine Classifier: LDA-MWSVM is introduced. The structure of this automatic system based on LDA-MWSVM for the diagnosis of diabetes is composed of three stages: The feature extraction and feature reduction stage by using the Linear Discriminant Analysis (LDA) method and the classification stage by using Morlet Wavelet Support Vector Machine (MWSVM) classifier stage. The Linear Discriminant Analysis (LDA) is used to separate features variables between healthy and patient (diabetes) data in the first stage. The healthy and patient (diabetes) features obtained in the first stage are given to inputs of the MWSVM classifier in the second stage. Finally, in the third stage, the correct diagnosis performance of this automatic system based on LDA-MWSVM for the diagnosis of diabetes is calculated by using sensitivity and specificity analysis, classification accuracy, and confusion matrix, respectively. The classification accuracy of this system was obtained at about 89.74%. (C) 2011 Elsevier Ltd. All rights reserved.
Keywords: Linear Discriminant Analysis; Morlet Wavelet Support Vector Machine; Classifier; Diabetes data; Automatic system; Sensitivity and specificity analysis; Classification accuracy; Confusion matrix