An intelligent diagnosis system for diabetes on Linear Discriminant Analysis and Adaptive Network Based Fuzzy Inference System: LDA-ANFIS

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%.

[1]  Bart Kosko,et al.  Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence , 1991 .

[2]  Kemal Polat,et al.  An expert system approach based on principal component analysis and adaptive neuro-fuzzy inference system to diagnosis of diabetes disease , 2007, Digit. Signal Process..

[3]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[4]  Emre Çomak,et al.  A decision support system based on support vector machines for diagnosis of the heart valve diseases , 2007, Comput. Biol. Medicine.

[5]  Chuen-Tsai Sun,et al.  Neuro-fuzzy modeling and control , 1995, Proc. IEEE.

[6]  T. Yıldırım,et al.  MEDICAL DIAGNOSIS ON PIMA INDIAN DIABETES USING GENERAL REGRESSION NEURAL NETWORKS , 2003 .

[7]  H. Dickhaus,et al.  Classifying biosignals with wavelet networks [a method for noninvasive diagnosis] , 1996 .

[8]  Nir Friedman,et al.  Bayesian Network Classifiers , 1997, Machine Learning.

[9]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[10]  Hai Tao,et al.  Fast Linear Discriminant Analysis Using Binary Bases , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[11]  Kemal Polat,et al.  A cascade learning system for classification of diabetes disease: Generalized Discriminant Analysis and Least Square Support Vector Machine , 2008, Expert Syst. Appl..

[12]  K. Bennett,et al.  A support vector machine approach to decision trees , 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).

[13]  Engin Avci,et al.  Intelligent Target Recognition Based on Wavelet Adaptive Network Based Fuzzy Inference System , 2005, IbPRIA.

[14]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[15]  Kemal Polat,et al.  Principles component analysis, fuzzy weighting pre-processing and artificial immune recognition system based diagnostic system for diagnosis of lung cancer , 2008, Expert Syst. Appl..

[16]  A. B. Watkins,et al.  A resource limited artificial immune classifier , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[17]  Engin Avci,et al.  Speech recognition using a wavelet packet adaptive network based fuzzy inference system , 2006, Expert Syst. Appl..

[18]  Engin Avci,et al.  A novel approach for digital radio signal classification: Wavelet packet energy-multiclass support vector machine (WPE-MSVM) , 2008, Expert Syst. Appl..