Performance of small-world feedforward neural networks for the diagnosis of diabetes

Applied Mathematics and Computation - Tập 311 - Trang 22-28 - 2017
Okan Erkaymaz1, Mahmut Ozer2, Matjaž Perc3,4
1Department of Computer Engineering, Bulent Ecevit University, Zonguldak, Turkey
2Department of Electrical & Electronics Engineering, Bulent Ecevit University, Zonguldak, Turkey
3Department of Physics, Faculty of Natural Sciences and Mathematics, University of Maribor, Koroška cesta 160, SI-2000 Maribor, Slovenia
4Center for Applied Mathematics and Theoretical Physics, University of Maribor, Mladinska 3, SI-2000 Maribor, Slovenia

Tài liệu tham khảo

World Bank, The W orld Development Indicators (WDI). World Bank . April 2010. Wang, 2013, Evaluating the risk of type 2 diabetes mellitus using artificial neural network: An effective classification approach, Diabetes Res. Clin. Pract., 100, 111, 10.1016/j.diabres.2013.01.023 Polonsky, 2012, The past 200 years in diabetes, New Engl. J. Med., 367, 1332, 10.1056/NEJMra1110560 Haykin, 1999 Gurney, 1997 Narasingarao, 2009, A clinical decision support system using multilayer perceptron neural network to assess wellbeing in diabetes, J. Assoc. Phys. India, 57 Li, 2012, Performance comparison between logistic regressions, decision trees and multilayer perceptron in predicting peripheral neuropathy in type 2 diabetes mellitus, Chin. Med. J., 125, 851 Rahman, 2013, Application of artificial neural network and binary logistic regression in detection of diabetes status, Sci. J. Public Health, 1, 39, 10.11648/j.sjph.20130101.16 Khashei, 2012, Diagnosing diabetes type II using a soft intelligent binary classification model, Rev. Bioinform. Biom., 1 Anand, 2012, Data pre-processing and neural network algorithms for diagnosis of type ii diabetes: a survey, Int. J. Eng. Adv. Technol., 2 Vosoulipour, 2008, Classification on diabetes mellitus dataset based-on artificial neural networks and ANFIS, 21, 27 Temirtas, 2009, A comparative study on diabetes disease diagnosis using neural network, Expert Syst Appl., 36, 8610, 10.1016/j.eswa.2008.10.032 Soltani, 2016, A new artificial neural networks approach for diagnosing diabetes disease type-2, Int. J. Adv. Comput. Sci. Appl., 7, 89 Watts, 1998, Collective dynamics of 'small-world' networks, Nature, 393, 440, 10.1038/30918 Latora, 2001, Efficient behavior of small-world networks, Phys. Rev. Lett., 87, 10.1103/PhysRevLett.87.198701 Uzuntarla, 2015, Vibrational resonance in a heterogeneous scale free network of neurons, Commun. Nonlinear Sci. Numer. Simul., 22, 367, 10.1016/j.cnsns.2014.08.040 Yilmaz, 2013, Stochastic resonance in hybrid scale-free neuronal networks, Physica A, 392, 5735, 10.1016/j.physa.2013.07.011 Ozer, 2008, Collective temporal coherence for subthreshold signal encoding on a stochastic small-world Hodgkin-Huxley neuronal network, Phys. Lett. A, 372, 6498, 10.1016/j.physleta.2008.09.007 Ozer, 2009, Controlling the spontaneous spiking regularity via channel blocking on Newman-Watts Networks of Hodgkin-Huxley neurons, Europhys. Lett., 86, 40008, 10.1209/0295-5075/86/40008 Yilmaz, 2016, Autaptic pacemaker mediated propagation of weak rhythmic activity across small-world neuronal networks, Physica A, 444, 538, 10.1016/j.physa.2015.10.054 Simard, 2005, Fastest learning in small-world neural networks, Phys. Lett. A, 336, 8, 10.1016/j.physleta.2004.12.078 Li, 2013, A multilayer feedforward small-world neural network controller and its application on electrohydraulic actuation system, J. Appl. Math., 2013 Erkaymaz, 2014, Impact of small-world topology on the performance of a feed-forward artificial neural network based on 2 different real-life problems, Turk. J. Elec. Eng. Comp. Sci., 22, 708, 10.3906/elk-1202-89 Erkaymaz, 2016, Impact of small-world network topology on the conventional artificial neural network for the diagnosis of diabetes, Chaos Sol. Fract., 83, 178, 10.1016/j.chaos.2015.11.029 Newman, 1999, Scaling and percolation in the small-world network model, Phys. Rev. E, 60, 7332, 10.1103/PhysRevE.60.7332 Newman, 2003, The structure and function of complex networks, SIAM Rev., 45, 167, 10.1137/S003614450342480 Bassett, 2006, Small-world brain networks, Neuroscientist, 12, 512, 10.1177/1073858406293182 Bullmore, 2009, Complex brain networks: graph theoretical analysis of structural and functional systems, Nat. Rev. Neurosci., 10, 186, 10.1038/nrn2575 C.L. Blake and C.J. Merz. UCI Repository Of Machine Learning Databases. Department of Information and Computer Sciences, University of California, Irvine, 1998 Available from: https://archive.ics.uci.edu/ml/datasets/Pima+Indians+Diabetes. Carpenter, 1998, ARTMAP-IC and medical diagnosis: instance counting and inconsistent cases, Neural Netw., 11, 323, 10.1016/S0893-6080(97)00067-1 Deng, 2001, On-line pattern analysis by evolving self-organizing maps, 46 Kayaer, 2003, Medical diagnosis on Pima Indians diabetes using general regression neural networks, 181 Polat, 2007, An expert system approach based on principal component analysis and adaptive neuro-fuzzy inference system to diagnosis of diabetes disease, Digit. Signal Process., 17, 702, 10.1016/j.dsp.2006.09.005 Polat K. Gunes, 2008, A cascade learning system for classification of diabetes disease: generalized discriminant analysis and least square support vector machine, Expert Syst. Appl., 34, 214 Stone, 1974, Cross-validation choice and assessment of statistical predictions (with discussion), J. R. Stat. Soc., 36, 111 Stehman, 1997, Selecting and interpreting measures of thematic classification accuracy, Remote Sens. Environ., 62, 77, 10.1016/S0034-4257(97)00083-7 Isler, 2015, Comparison of the effects of cross-validation methods on determining performances of classifiers used in diagnosing congestive heart failure, Meas. Sci. Rev., 15, 196, 10.1515/msr-2015-0027 Aram, 2017, Using chaotic artificial neural networks to model memory in the brain, Commun. Nonlinear Sci. Numer. Simul., 44, 449, 10.1016/j.cnsns.2016.08.025 Fister, 2016, Artificial neural network regression as a local search heuristic for ensemble strategies in differential evolution, Nonlinear Dyn., 84, 895, 10.1007/s11071-015-2537-8 Fister, 2015, Computational intelligence in sports: challenges and opportunities within a new research domain, Appl. Math. Comput., 262, 178