Publisher : The Korean Institute of Electrical Engineers
DOI : 10.5370/JEET.2016.11.4.993
Title & Authors
An Automatic Diagnosis System for Hepatitis Diseases Based on Genetic Wavelet Kernel Extreme Learning Machine Avci, Derya;
Hepatitis is a major public health problem all around the world. This paper proposes an automatic disease diagnosis system for hepatitis based on Genetic Algorithm (GA) Wavelet Kernel (WK) Extreme Learning Machines (ELM). The classifier used in this paper is single layer neural network (SLNN) and it is trained by ELM learning method. The hepatitis disease datasets are obtained from UCI machine learning database. In Wavelet Kernel Extreme Learning Machine (WK-ELM) structure, there are three adjustable parameters of wavelet kernel. These parameters and the numbers of hidden neurons play a major role in the performance of ELM. Therefore, values of these parameters and numbers of hidden neurons should be tuned carefully based on the solved problem. In this study, the optimum values of these parameters and the numbers of hidden neurons of ELM were obtained by using Genetic Algorithm (GA). The performance of proposed GA-WK-ELM method is evaluated using statical methods such as classification accuracy, sensitivity and specivity analysis and ROC curves. The results of the proposed GA-WK-ELM method are compared with the results of the previous hepatitis disease studies using same database as well as different database. When previous studies are investigated, it is clearly seen that the high classification accuracies have been obtained in case of reducing the feature vector to low dimension. However, proposed GA-WK-ELM method gives satisfactory results without reducing the feature vector. The calculated highest classification accuracy of proposed GA-WK-ELM method is found as 96.642 %.
Pattern recognition;Wavelet Kernel (WK) based Extreme Learning Machines (ELM);Genetic Algorithm (GA);Classification accuracy;Sensitivity and specivity analysis;ROC curves;Hepatitis;
Hybrid fuzzy polynomial neural networks with the aid of weighted fuzzy clustering method and fuzzy polynomial neurons, Applied Intelligence, 2016
Esin Dogantekin, Akif Dogantekin, Derya Avci, Automatic hepatitis diagnosis system based on Linear Discriminant Analysis and Adaptive Network based on Fuzzy Inference System, Expert Systems with Applications 36 (2009) 11282–11286.
Esin Dogantekin, Akif Dogantekin, and Derya Avci. “Automatic hepatitis diagnosis system based on linear discriminant analysis and adaptive network based on fuzzy inference system”, Expert Systems with Applications 36(8), pp. 11282-11286, 2009.
Tahseen A. Jilani, Huda Yasin, Madiha Mohammad Yasin, “PCA-ANN for Classification of Hepatitis-C Patients”, International Journal of Computer Applications (0975-8887), Volume 14-No.7, February 2011.
Ultrasound and Magnetic Resonance Medical Images Textures Analysis Using Neural Networks, http://www.intermag.kiev.ua/projects/neuraln.html, (last accessed: 20 January 2012).
M. Serdar Bascil, Feyzullah Temurtas, “A Study on Hepatitis Disease Diagnosis Using Multilayer Neural Network with Levenberg Marquardt Training Algorithm”, J Med Syst (2011) 35:433-436.
Adeli, Mahdieh, and Hassan Zarabadipour. “Automatic disease diagnosis systems using pattern recognition based genetic algorithm and neural networks”, Int. J. Phys. Sci. v6 i25 (2011): 6076-6081.
Bin Li, Xuewen Rong and Yibin Li, "An Improved Kernel Based Extreme Learning Machine for Robot Execution Failures", Hindawi Publishing Corporation The Scientific World Journal, Volume 2014, Article ID 906546, pp. 7, http://dx.doi.org/10.1155/2014/906546.
Peng Guan, De-Sheng Huang, Bao-Sen Zhou, Forecasting model for the incidence of hepatitis A based on artificial neural network, China World Journal of Gastroenterol; 10(24), 2004, pp. 3579-3582.
Kemal Polat, Salih Gunes, Hepatitis disease diagnosis using a new hybrid system based on feature selection (FS) and artificial immune recognition system with fuzzy resource allocation, Digital Signal Processing 16 (2006), pp. 889-901.
Duygu Çalisir, Esin Dogantekin, A new intelligent hepatitis diagnosis system: PCA-LSSVM, Expert Systems with Applications, Volume 38, Issue 8, pages: 10705-10708, 2011.
Al-Shayea, Qeethara Kadhim. “Artificial neural net- works in medical diagnosis”, International Journal of Computer Science Issues 8.2 (2011): 150-154.
Amato, Filippo, et al. “Artificial neural networks in medical diagnosis”, Journal of applied biomedicine 11.2 (2013): 47-58.
Malmgren, Helge, Magnus Borga, and Lars Niklasson, eds. Artificial Neural Networks in Medicine and Biology: Proceedings of the ANNIMAB-1 Conference, Göteborg, Sweden, 13-16 May 2000. Springer Science & Business Media, 2012.
Cho, Jae-Hoon, Dae-Jong Lee, and Myung-Geun Chun, "Parameter Optimization of Extreme Learning Machine Using Bacterial Foraging Algorithm", EESRI, pp: 742-747, 2007.
Guang-Bin, Huang, et al. “Extreme learning machine for regression and multiclass classification.” Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on 42.2 (2012): 513-529.
Guang-Bin Huang, et al., “Extreme Learning Machines: A Survey,” International Journal of Machine Leaning and Cybernetics, 2 (2011) 107-1221.
Guang-Bin Huang, et al., “Optimization Method Based Extreme Learning Machine for Classification”, Neurocomputing, 74 (2010) 155-163.
Huang, Guang-Bin, Lei Chen, and Chee-Kheong Siew, “Universal Approximation Using Incremental Constructive Feedforward Networks with Random Hidden Nodes”, IEEE Transactions on Neural Networks, 17 (2006) 879-892.
Guang-Bin Huang, et al., “Extreme Learning Machine: A New Learning Scheme of Feedforward Neural Networks,” 2004 International Joint Conference on Neural Networks (IJCNN'2004), (Budapest, Hungary), July 25-29, 2004.
Guang-Bin Huang, et al., “Extreme Learning Machine: Theory and Applications”, Neurocomputing, vol. 70, pp. 489-501, 2006.
Liang, Nan-Ying, et al., “A Fast and Accurate On-line Sequential Learning Algorithm for Feedforward Networks”, IEEE Transactions on Neural Networks, 17 (2006) 1411-1423.
Guo, Ling, et al., “Automatic feature extraction using genetic programming: An application to epileptic EEG classification”, Expert Systems with Applications, Volume 38, Issue 8, August 2011, Pages 10425-10436.
Birendra Biswal, et al., “Non-stationary power signal processing for pattern recognition using HS-transform”, Applied Soft Computing, Volume 9, Issue 1, January 2009, Pages 107-117.
Jiang, Lingli, et al., “Using bispectral distribution as a feature for rotating machinery fault diagnosis”, Measurement, Volume 44, Issue 7, August 2011, Pages 1284-1292.
UCI Machine Learning Repository. Available from: http://www.ics.uci.edu/-mlearn/MLRepository.html (last accessed: 20 September 2014).