An automatic diabetes diagnosis system based on LDA-Wavelet Support Vector Machine Classifier
| Autor: | Esin Dogantekin, Duygu Çalişir |
|---|---|
| Rok vydání: | 2011 |
| Předmět: |
Structured support vector machine
Computer science business.industry Speech recognition Feature extraction General Engineering Confusion matrix Pattern recognition Linear classifier Quadratic classifier Linear discriminant analysis Computer Science Applications Support vector machine Morlet wavelet Artificial Intelligence Artificial intelligence business |
| Zdroj: | Expert Systems with Applications. 38:8311-8315 |
| ISSN: | 0957-4174 |
| DOI: | 10.1016/j.eswa.2011.01.017 |
| Popis: | 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%. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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