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An intelligent diagnosis system for diabetes on Linear Discriminant Analysis and Adaptive Network Based Fuzzy Inference System: LDA-ANFIS by Esin Dogantekin; Akif Dogantekin; Derya Avci; Levent Avci is a Computer Science article available to read on EtoBox.

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In this study, an intelligent diagnosis system for diabetes on Linear Discriminant Analysis (LDA) and Adaptive Network Based Fuzzy Inference System (ANFIS): LDA-ANFIS is presented. The structure of this LDA-ANFIS intelligent system for diagnosis of diabetes is composed by two phases: The Linear Discriminant Analysis (LDA) phase and classificiation by using ANFIS classifier phase. In first phase, Linear Discriminant Analysis (LDA) is used to separate features variables between healthy and patient (diabetes) data. In second phase, the healthy and patient (diabetes) features obtained in first phase are given to inputs of ANFIS classifier. The correct diagnosis performance of the LDA-ANFIS intelligent system is calculated by using sensitivity and specificity analysis, classification accuracy and confusion matrix respectively. The classification accuracy of this LDA-ANFIS intelligent system was obtained about 84.61%.

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Author

Esin Dogantekin; Akif Dogantekin; Derya Avci; Levent Avci

Publisher

Elsevier Science; Elsevier ; Elsevier Inc.; Elsevier BV (ISSN 1051-2004)

Published

2010

Language

EN

Field

Computer Science (Physical Sciences)