Proposed method with all indicators
|
98.47% |
|
GA and Prot. Selec. |
92.60% |
Byeon et al. (2008)Byeon B, Rasheed K, Doshi P. Enhancing the quality of noisy training data using
a genetic algorithm and prototype selection. In: Proceedings of the 2008 International
Conference on Artificial Intelligence; 2008 July 14-17, Las Vegas, Nevada. 2008. p.
821-7.
|
HPM |
92.38% |
Patil et al. (2010)Patil BM, Joshi RC, Toshniwal D. Hybrid prediction model for type-2 diabetic
patients. Expert Systems with Applications 2010; 37(12):8102-8.
http://dx.doi.org/10.1016/j.eswa.2010.05.078. http://dx.doi.org/10.1016/j.eswa.2010.05...
|
Fuzzy |
91.20% |
Lee and Wang (2011)Lee C-S, Wang M-H. A fuzzy expert system for diabetes decision support
application. IEEE Transactions on Man and Cybernetics, Part B. 2011; 41(1):139-53.
http://dx.doi.org/10.1109/TSMCB.2010.2048899. http://dx.doi.org/10.1109/TSMCB.2010.204...
|
LDA-Wavelet SVM |
89.74% |
Çalişir and Doğantekin (2011)Çalişir D, Doğantekin E. An automatic diabetes diagnosis system based on LDA
wavelet support vector machine classifier. Expert Systems with Applications 2011; 38(7):8311-5.
http://dx.doi.org/10.1016/j.eswa.2011.01.017. http://dx.doi.org/10.1016/j.eswa.2011.01...
|
IFE-CF |
89.48% |
Reddy and Reddy (2010)Reddy MB, Reddy LSS. Dimensionality reduction: an empirical study on the
usability of IFECF (independent feature elimination- by c-correlation and f- correlation)
measures. International Journal of Computer Science. 2010; 7(1):74-81.
|
PCA-ANFIS |
89.47% |
Polat and Gunes (2007) |
MAIRS2 |
89.10% |
Chikh et al. (2012)Chikh MA, Saidi M, Settouti N. Diagnosis of diabetes diseases using an
Artificial Immune Recognition System2 (AIRS2) with fuzzy K-nearest neighbor. Journal of Medical
Systems 2012; 36(5):2721-9. http://dx.doi.org/10.1007/s10916-011-9748-4.
PMid:21695498 http://dx.doi.org/10.1007/s10916-011-974...
|
LDA-ANFIS |
84.61% |
Dogantekin et al. (2010)Dogantekin E, Dogantekin A, Avci D, Avci L. An intelligent diagnosis system for
diabetes on linear discriminant analysis and adaptive network based fuzzy inference system:
LDA-ANFIS. Digital Signal Processing 2010; 20(4):1248-55.
http://dx.doi.org/10.1016/j.dsp.2009.10.021. http://dx.doi.org/10.1016/j.dsp.2009.10....
|
ANN-FNN |
84.24% |
Kahramanli and Allahverdi (2008)Kahramanli H, Allahverdi N. Design of a hybrid system for the diabetes and heart
diseases. Expert Systems with Applications 2008; 35(1-2):82-9.
http://dx.doi.org/10.1016/j.eswa.2007.06.004. http://dx.doi.org/10.1016/j.eswa.2007.06...
|
GDA-LS-SVM |
82.05% |
Polat et al. (2008)Polat K, Gunes S, Arslan A. A cascade learning system for classification of
diabetes disease: generalized discriminant analysis and least square support vector machine.
Expert Systems with Applications 2008; 34(1):482-7.
http://dx.doi.org/10.1016/j.eswa.2006.09.012. http://dx.doi.org/10.1016/j.eswa.2006.09...
|
C-HMLP |
81.74% |
Mat Isa and Mamat (2011)Mat Isa NA, Mamat WMFW. Clustered-hybrid multilayer perceptron network for
pattern recognition application. Applied Soft Computing 2011; 11(1):1457-66.
http://dx.doi.org/10.1016/j.asoc.2010.04.017. http://dx.doi.org/10.1016/j.asoc.2010.04...
|
Fuzzy |
79.37% |
Lekkas and Mikhailov (2010)Lekkas S, Mikhailov L. Evolving fuzzy medical diagnosis of Pima Indians diabetes
and of dermatological diseases. Artificial Intelligence in Medicine 2010; 50(2):117-26.
http://dx.doi.org/10.1016/j.artmed.2010.05.007. PMid:20566274 http://dx.doi.org/10.1016/j.artmed.2010....
|
ANFIS |
77.65% |
Ghazavi and Liao (2008)Ghazavi SN, Liao TW. Medical data mining by fuzzy modeling with selected
features. Artificial Intelligence in Medicine 2008; 43(3):195-206.
http://dx.doi.org/10.1016/j.artmed.2008.04.004. PMid:18534831 http://dx.doi.org/10.1016/j.artmed.2008....
|
Fuzzy |
77.8% |
Luukka (2011a)Luukka P. Feature selection using fuzzy entropy measures with similarity
classifier. Expert Systems with Applications 2011a; 38(4):4600-7.
http://dx.doi.org/10.1016/j.eswa.2010.09.133. http://dx.doi.org/10.1016/j.eswa.2010.09...
|
ANN |
76.62% |
Jeatrakul et al. (2010)Jeatrakul P, Wong KW, Fung CC. Data cleaning for classification using
misclassification analysis. Journal of Advanced Computational Intelligence and Intelligent
Informatics. 2010; 14(3):297-302.
|
OP-ELM |
76.3% |
Miche et al. (2010)Miche Y, Sorjamaa A, Bas P, Simula O, Jutten C, Lendasse A. OP-ELM: optimally
pruned extreme learning machine. IEEE Transactions on Neural Networks 2010; 21(1):158-62.
http://dx.doi.org/10.1109/TNN.2009.2036259. PMid:20007026 http://dx.doi.org/10.1109/TNN.2009.20362...
|
IP-LSSVM |
76.1% |
Carvalho and Braga (2009)Carvalho BPRD, Braga AP. IP-LSSVM: A two-step sparse classifier. Pattern
Recognition Letters 2009; 30(16):1507-15.
http://dx.doi.org/10.1016/j.patrec.2009.07.022. http://dx.doi.org/10.1016/j.patrec.2009....
|
FSM-FuzzyEM |
75.97% |
Luukka (2011b)Luukka P. Fuzzy beans in classification. Expert Systems with Applications 2011b;
38(5):4798-801. http://dx.doi.org/10.1016/j.eswa.2010.09.167. http://dx.doi.org/10.1016/j.eswa.2010.09...
|
SVM |
75.15% |
Li and Liu (2010)Li D, Liu C. A class possibility based kernel to increase classification
accuracy for small data sets using support vector machines. Expert Systems with Applications
2010; 37(4):3104-10. http://dx.doi.org/10.1016/j.eswa.2009.09.019. http://dx.doi.org/10.1016/j.eswa.2009.09...
|