An application of multilayer neural network on hepatitis disease diagnosis using approximations of sigmoid activation function
Öz
Objective: Implementation of multilayer neural network (MLNN) with sigmoid activation function for the diagnosis of hepatitis disease.
Methods: Artificial neural networks (ANNs) are efficient tools currently in common use for medical diagnosis. In hardware based architectures activation functions play an important role in ANN behavior. Sigmoid function is the most frequently used activation function because of its smooth response. Thus, sigmoid function and its close approximations were implemented as activation function. The dataset is taken from the UCI machine learning database.
Results: For the diagnosis of hepatitis disease, MLNN structure was implemented and Levenberg Morquardt (LM) algorithm was used for learning. Our method of classifying hepatitis disease produced an accuracy of 91.9% to 93.8% via 10 fold cross validation.
Conclusion: When compared to previous work that diagnosed hepatitis disease using artificial neural networks and the identical data set, our results are promising in order to reduce the size and cost of neural network based hardware. Thus, hardware based diagnosis systems can be developed effectively by using approximations of sigmoid function.
Key words: Hepatitis disease diagnosis, multilayer neural network, 10-fold cross validation, approximations of sigmoid activation function
Anahtar Kelimeler
Kaynakça
- Chen H-L, et al. A new hybrid method based on local
- fisher discriminant analysis and support vector machines for hepatitis disease diagnosis. Expert Syst Applicat 2011;38:11796-11803.
- Ansari S, et al. Diagnosis of liver disease induced by hepatitis
- virus using artificial neural networks. Multitopic Conference (INMIC), 2011 IEEE 14th International 2011;8-12.
- Polat K, Gunes S. A hybrid approach to medical decision support systems: combining feature selection, fuzzy weighted
- pre-processing and AIRS. Comput Methods Programs
- Biomed 2007;88:164-174.
- Dogantekin E, Dogantekin A, Avci D. Automatic hepatitis
- diagnosis system based on Linear Discriminant Analysis
- and Adaptive Network based on Fuzzy Inference System.
- Expert Syst Applicat 2009;36:11282-11286.
- Calisir D, Dogantekin E. A new intelligent hepatitis diagnosis system: PCA LSSVM. Expert Syst Applicat
- ;38:10705-10708.
- Sartakhti JS, et al. Hepatitis disease diagnosis using a novel
- hybrid method based on support vector machine and simulated annealing (SVM-SA). Comput Methods and Programs in Biomed 2011.
- Ozyılmaz L, Yıldırım T. Artificial neural networks for diagnosis of hepatitis disease, in: International Joint Conference
- on Neural Networks (IJCNN) 2003;1:586-589.
- http://www.is.umk.pl/projects/datasets.html
- Polat K, Gunes S. Hepatitis disease diagnosis using a new
- hybrid system based on feature selection (FS) and artificial
- immune recognition system with fuzzy resource allocation.
- Digital Signal Process 2006;16:889-901.
- Polat K, Gunes S. Medical decision support system based
- on artificial immune recognition immune system (AIRS),
- fuzzy weighted pre-processing and feature selection. Expert Syst Applicat 2007;33:484-490.
- Bascil MS, Temurtas F. A study on hepatitis disease diagnosis using multilayer neural network with Levenberg Marquardt Training Algorithm. J Med Syst 2011;35:433-436.
- ich W, et al. Minimal distance neural methods. Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint
- Conference on, 1998;2:1299-1304.
- Duch W, Adamczak R, Grabczewski K. Optimization of
- logical rules derived by neural procedures. Neural Networks, 1999. IJCNN ‘99. International Joint Conference
- on, 1999;1:669-674.
- Ster B, Dobnikar A. Neural Networks in Medical Diagnosis: Comparison with Other Methods. Proceedings of the
- International Conference EANN96 1996;1:427-430.
- Tan KC, et al. A hybrid evolutionary algorithm for attribute selection in data mining. Expert Syst Applicat
- ;36:8616-8630.
- Bascil MS, Oztekin H. A study on hepatitis disease diagnosis using probabilistic neural network. J Med Syst 2010.
- Er O, Tanrikulu AC, Abakay A. Use of artificial intelligence
- techniques for diagnosis of malignant pleural mesothelioma. Dicle Medical Journal 2015;42:467-470.
- Haykin S. Neural Networks: A Comprehensive Foundation.
- New York, Macmillan Publishing 1994.
- Kayaer K, Yıldırım T. Medical diagnosis on Pima Indian
- Diabetes using general regression neural networks. In Proc.
- of International Conference on Artificial Neural Networks
- and Neural Information Processing (ICANN/ICONIP), Istanbul:181-184.
- Delen D, Walker G, Kadam A. Predicting breast cancer
- survivability: A comparison of three data mining methods.
- Artificial Intelligence in Medicine 2005;34:113-127.
- Temurtas F. A comparative study on thyroid disease diagnosis using neural networks. Expert Syst Applicat
- ;36:944-949.
- Er O, Temurtas F. A study on chronic obstructive pulmonary
- disease diagnosis using multilayer neural networks. J Med
- Syst 2008;32:429-432.
- Rumelhart DE, Hinton GE. Williams RJ. Learning internal
- representations by error propagation. In Rumelhart DE, and
- McClelland JL. (Eds.) Parallel Distributed Processing: Explorations in the Microstructure of Cognition, MIT Press, Cambridge, MA, 1986;1:318-362.
- Brent RP. Fast training algorithms for multilayer neural
- nets. IEEE Trans. Neural Networks 1991;2:346-354.
- Gori M, Tesi A. On the problem of local minima in backpropagation. IEEE Trans Pattern Anal Machine Intell
- ;14:76-86.
- Hagan MT, Menhaj M. Training feed forward networks
- with the Marquardt algorithm. IEEE Trans Neural Networks 1994;5:989-993.
- Hagan MT, Demuth HB, Beale MH. Neural Network Design, PWS Publishing, Boston, MA, 1996.
- Gulbag A, Temurtas F. A study on quantitative classification of binary gas mixture using neural networks and adaptive neuro fuzzy inference systems. Sens Actuators B 2006;115:252-262.
- Rumelhart DE, et al. Backpropagation: The basic theory.
- In: Smolensky P, Mozer MC, Rumelhart DE. (Eds.) Mathematical Perspectives on Neural Networks, Hillsdale, NJ, Erlbaum, 1996;533-566.
- Ozdemir AT, Danisman K. Fully parallel ANN-based arrhythmia classifier on a single-chip FPGA: FPAAC. Turkish Journal of Elec Eng and Computer Sci 2011;19:667 687.
- http://archive.ics.uci.edu/ml/datasets/Hepatitis, last accessed: 20 March 2013.
- Wilamowski BM, Yu H. Improved Computation for Levenberg-Marquardt Training IEEE Trans Neural Networks 2010;21:930-937.
- Watkins A. AIRS: A resource limited artificial immune classifier. Master Thesis, Mississippi State University, 2001.
- Myers DJ, Hutchinson RA. Efficient implementation of
- piecewise linear activation function for digital VLSI neural
- Networks. Electronics Letters 1989;25:1662-1663.
- Bharkhada BK. Efficient FPGA implementation of a generic
- function approximator and its application to neural net computation. Master Thesis, University of Cincinnati, 2003.
- Nordström T, Svensson B. Using and designing massively
- parallel computers for artificial neural networks. Journal of
- Parallel and Distributed Computing 1992;14:260 285.
- Amin H, Curtis KM, Hayes-Gill BR. Piecewise linear approximation applied to nonlinear function of a neural
- network. IEE Proceedings-Circuits Devices and Systems
- ;144:313-317.
- Tommiska MT. Efficient digital implementation of the sigmoid function for reprogrammable logic. IEE ProceedingsComputers and Digital Techniques 2003;150:403-411.
- Arroyo Leon MAA, Ruiz Castro A, Leal Ascencio RR. An artificial neural network on a field programmable gate array
- as a virtual sensor. Design of Mixed-Mode Integrated Circuits and Applications, 1999. Third International Workshop on, 1999;114-117.
- Temurtas H, Yumusak N, Temurtas F. A comparative study
- on diabetes disease diagnosis using neural networks. Expert
- Syst Applicat 2009;36:8610-8615.
An application of multilayer neural network on hepatitis disease diagnosis using approximations of sigmoid activation function
Öz
Objective: Implementation of multilayer neural network (MLNN) with sigmoid activation function for the diagnosis of hepatitis disease.
Methods: Artificial neural networks (ANNs) are efficient tools currently in common use for medical diagnosis. In hardware based architectures activation functions play an important role in ANN behavior. Sigmoid function is the most frequently used activation function because of its smooth response. Thus, sigmoid function and its close approximations were implemented as activation function. The dataset is taken from the UCI machine learning database.
Results: For the diagnosis of hepatitis disease, MLNN structure was implemented and Levenberg Morquardt (LM) algorithm was used for learning. Our method of classifying hepatitis disease produced an accuracy of 91.9% to 93.8% via 10 fold cross validation.
Conclusion: When compared to previous work that diagnosed hepatitis disease using artificial neural networks and the identical data set, our results are promising in order to reduce the size and cost of neural network based hardware. Thus, hardware based diagnosis systems can be developed effectively by using approximations of sigmoid function.
Key words: Hepatitis disease diagnosis, multilayer neural network, 10-fold cross validation, approximations of sigmoid activation function
Anahtar Kelimeler
Kaynakça
- Chen H-L, et al. A new hybrid method based on local
- fisher discriminant analysis and support vector machines for hepatitis disease diagnosis. Expert Syst Applicat 2011;38:11796-11803.
- Ansari S, et al. Diagnosis of liver disease induced by hepatitis
- virus using artificial neural networks. Multitopic Conference (INMIC), 2011 IEEE 14th International 2011;8-12.
- Polat K, Gunes S. A hybrid approach to medical decision support systems: combining feature selection, fuzzy weighted
- pre-processing and AIRS. Comput Methods Programs
- Biomed 2007;88:164-174.
- Dogantekin E, Dogantekin A, Avci D. Automatic hepatitis
- diagnosis system based on Linear Discriminant Analysis
- and Adaptive Network based on Fuzzy Inference System.
- Expert Syst Applicat 2009;36:11282-11286.
- Calisir D, Dogantekin E. A new intelligent hepatitis diagnosis system: PCA LSSVM. Expert Syst Applicat
- ;38:10705-10708.
- Sartakhti JS, et al. Hepatitis disease diagnosis using a novel
- hybrid method based on support vector machine and simulated annealing (SVM-SA). Comput Methods and Programs in Biomed 2011.
- Ozyılmaz L, Yıldırım T. Artificial neural networks for diagnosis of hepatitis disease, in: International Joint Conference
- on Neural Networks (IJCNN) 2003;1:586-589.
- http://www.is.umk.pl/projects/datasets.html
- Polat K, Gunes S. Hepatitis disease diagnosis using a new
- hybrid system based on feature selection (FS) and artificial
- immune recognition system with fuzzy resource allocation.
- Digital Signal Process 2006;16:889-901.
- Polat K, Gunes S. Medical decision support system based
- on artificial immune recognition immune system (AIRS),
- fuzzy weighted pre-processing and feature selection. Expert Syst Applicat 2007;33:484-490.
- Bascil MS, Temurtas F. A study on hepatitis disease diagnosis using multilayer neural network with Levenberg Marquardt Training Algorithm. J Med Syst 2011;35:433-436.
- ich W, et al. Minimal distance neural methods. Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint
- Conference on, 1998;2:1299-1304.
- Duch W, Adamczak R, Grabczewski K. Optimization of
- logical rules derived by neural procedures. Neural Networks, 1999. IJCNN ‘99. International Joint Conference
- on, 1999;1:669-674.
- Ster B, Dobnikar A. Neural Networks in Medical Diagnosis: Comparison with Other Methods. Proceedings of the
- International Conference EANN96 1996;1:427-430.
- Tan KC, et al. A hybrid evolutionary algorithm for attribute selection in data mining. Expert Syst Applicat
- ;36:8616-8630.
- Bascil MS, Oztekin H. A study on hepatitis disease diagnosis using probabilistic neural network. J Med Syst 2010.
- Er O, Tanrikulu AC, Abakay A. Use of artificial intelligence
- techniques for diagnosis of malignant pleural mesothelioma. Dicle Medical Journal 2015;42:467-470.
- Haykin S. Neural Networks: A Comprehensive Foundation.
- New York, Macmillan Publishing 1994.
- Kayaer K, Yıldırım T. Medical diagnosis on Pima Indian
- Diabetes using general regression neural networks. In Proc.
- of International Conference on Artificial Neural Networks
- and Neural Information Processing (ICANN/ICONIP), Istanbul:181-184.
- Delen D, Walker G, Kadam A. Predicting breast cancer
- survivability: A comparison of three data mining methods.
- Artificial Intelligence in Medicine 2005;34:113-127.
- Temurtas F. A comparative study on thyroid disease diagnosis using neural networks. Expert Syst Applicat
- ;36:944-949.
- Er O, Temurtas F. A study on chronic obstructive pulmonary
- disease diagnosis using multilayer neural networks. J Med
- Syst 2008;32:429-432.
- Rumelhart DE, Hinton GE. Williams RJ. Learning internal
- representations by error propagation. In Rumelhart DE, and
- McClelland JL. (Eds.) Parallel Distributed Processing: Explorations in the Microstructure of Cognition, MIT Press, Cambridge, MA, 1986;1:318-362.
- Brent RP. Fast training algorithms for multilayer neural
- nets. IEEE Trans. Neural Networks 1991;2:346-354.
- Gori M, Tesi A. On the problem of local minima in backpropagation. IEEE Trans Pattern Anal Machine Intell
- ;14:76-86.
- Hagan MT, Menhaj M. Training feed forward networks
- with the Marquardt algorithm. IEEE Trans Neural Networks 1994;5:989-993.
- Hagan MT, Demuth HB, Beale MH. Neural Network Design, PWS Publishing, Boston, MA, 1996.
- Gulbag A, Temurtas F. A study on quantitative classification of binary gas mixture using neural networks and adaptive neuro fuzzy inference systems. Sens Actuators B 2006;115:252-262.
- Rumelhart DE, et al. Backpropagation: The basic theory.
- In: Smolensky P, Mozer MC, Rumelhart DE. (Eds.) Mathematical Perspectives on Neural Networks, Hillsdale, NJ, Erlbaum, 1996;533-566.
- Ozdemir AT, Danisman K. Fully parallel ANN-based arrhythmia classifier on a single-chip FPGA: FPAAC. Turkish Journal of Elec Eng and Computer Sci 2011;19:667 687.
- http://archive.ics.uci.edu/ml/datasets/Hepatitis, last accessed: 20 March 2013.
- Wilamowski BM, Yu H. Improved Computation for Levenberg-Marquardt Training IEEE Trans Neural Networks 2010;21:930-937.
- Watkins A. AIRS: A resource limited artificial immune classifier. Master Thesis, Mississippi State University, 2001.
- Myers DJ, Hutchinson RA. Efficient implementation of
- piecewise linear activation function for digital VLSI neural
- Networks. Electronics Letters 1989;25:1662-1663.
- Bharkhada BK. Efficient FPGA implementation of a generic
- function approximator and its application to neural net computation. Master Thesis, University of Cincinnati, 2003.
- Nordström T, Svensson B. Using and designing massively
- parallel computers for artificial neural networks. Journal of
- Parallel and Distributed Computing 1992;14:260 285.
- Amin H, Curtis KM, Hayes-Gill BR. Piecewise linear approximation applied to nonlinear function of a neural
- network. IEE Proceedings-Circuits Devices and Systems
- ;144:313-317.
- Tommiska MT. Efficient digital implementation of the sigmoid function for reprogrammable logic. IEE ProceedingsComputers and Digital Techniques 2003;150:403-411.
- Arroyo Leon MAA, Ruiz Castro A, Leal Ascencio RR. An artificial neural network on a field programmable gate array
- as a virtual sensor. Design of Mixed-Mode Integrated Circuits and Applications, 1999. Third International Workshop on, 1999;114-117.
- Temurtas H, Yumusak N, Temurtas F. A comparative study
- on diabetes disease diagnosis using neural networks. Expert
- Syst Applicat 2009;36:8610-8615.
Toplam 86 adet kaynakça vardır.