An application of multilayer neural network on hepatitis disease diagnosis using approximations of sigmoid activation function

Yıl 2015, Cilt: 42 Sayı: 2, 150 - 157, 08.07.2015

Onursal Çetin , Feyzullah Temurtaş Şenol Gülgönül

https://doi.org/10.5798/diclemedj.0921.2015.02.0550

Cited By: 11

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.

Anahtar Kelimeler

Hepatitis disease diagnosis, multilayer neural network, 10-fold cross validation, approximations of sigmoid activation function

Sigmoid aktivasyon fonksiyonu kestirimi kullanılarak karaciğer hastalığı tanısında çok katmanlı sinir ağı uygulaması

Öz

Amaç: Hepatit hastalığının teşhisi için çok katmanlı sinir ağı (MLNN) ve sigmoid aktivasyon fonksiyonu uygulanmıştır.

Yöntemler: Yapay sinir ağları (YSA) tıbbi tanı için halen yaygın olarak kullanılan etkili araçlardır. Donanım tabanlı mimarilerde aktivasyon fonksiyonları YSA davranışında önemli rol oynamaktadır. Sigmoid fonksiyonu yumuşak tepkisi nedeniyle en sık kullanılan aktivasyon fonksiyonudur. Bu nedenle, sigmoid fonksiyonu ve yaklaşımları aktivasyon fonksiyonu olarak uygulanmıştır. Veri kümesi UCI makine öğrenme veri tabanından alınmıştır.

Bulgular: Hepatit hastalığının tanısı için, MLNN yapısı hayata geçirilmiş ve Levenberg Morquardt (LM) algoritması öğrenme için kullanılmıştır. Hepatit hastalığını sınıflandıran yöntemimiz 10-kat çapraz doğrulama yoluyla 91.9%’den 93.8%’e doğruluklar sağlamıştır.

Sonuç: Yapay sinir ağları ve aynı veri setini kullanarak hepatit hastalığını teşhis eden önceki çalışma ile karşılaştırıldığında, bizim sonuçlarımız sinir ağı tabanlı donanımın boyutunu ve maliyetini azaltması bakımından umut vericidir. Böylece, donanım tabanlı tanı sistemleri sigmoid fonksiyonu yaklaşımları kullanılarak etkili bir şekilde geliştirilebilir.

Anahtar Kelimeler

Hepatit hastalığı tanısı, çok katmanlı sinir ağı, 10-kat çapraz doğrulama, sigmoid aktivasyon fonksiyonu yaklaşımları

Kaynakça

• 1. 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.
• 2. 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.
• 3. 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.
• 4. 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.
• 5. Calisir D, Dogantekin E. A new intelligent hepatitis diagnosis system: PCA LSSVM. Expert Syst Applicat 2011;38:10705-10708.
• 6. 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.
• 7. Ozyılmaz L, Yıldırım T. Artificial neural networks for diagnosis of hepatitis disease, in: International Joint Conference on Neural Networks (IJCNN) 2003;1:586-589.
• 8. http://www.is.umk.pl/projects/datasets.html
• 9. 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.
• 10. 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.
• 11. 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.
• 12. 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
• 13. 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.
• 14. Ster B, Dobnikar A. Neural Networks in Medical Diagnosis: Comparison with Other Methods. Proceedings of the International Conference EANN96 1996;1:427-430.
• 15. Tan KC, et al. A hybrid evolutionary algorithm for attribute selection in data mining. Expert Syst Applicat 2009;36:8616-8630.
• 16. Bascil MS, Oztekin H. A study on hepatitis disease diagnosis using probabilistic neural network. J Med Syst 2010.
• 17. Er O, Tanrikulu AC, Abakay A. Use of artificial intelligence techniques for diagnosis of malignant pleural mesothelioma. Dicle Medical Journal 2015;42:467-470.
• 18. Haykin S. Neural Networks: A Comprehensive Foundation. New York, Macmillan Publishing 1994.
• 19. 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.
• 20. 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.
• 21. Temurtas F. A comparative study on thyroid disease diagnosis using neural networks. Expert Syst Applicat 2009;36:944-949.
• 22. Er O, Temurtas F. A study on chronic obstructive pulmonary disease diagnosis using multilayer neural networks. J Med Syst 2008;32:429-432.
• 23. 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.
• 24. Brent RP. Fast training algorithms for multilayer neural nets. IEEE Trans. Neural Networks 1991;2:346-354.
• 25. Gori M, Tesi A. On the problem of local minima in backpropagation. IEEE Trans Pattern Anal Machine Intell 1992;14:76-86.
• 26. Hagan MT, Menhaj M. Training feed forward networks with the Marquardt algorithm. IEEE Trans Neural Networks 1994;5:989-993.
• 27. Hagan MT, Demuth HB, Beale MH. Neural Network Design, PWS Publishing, Boston, MA, 1996.
• 28. 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.
• 29. 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.
• 30. 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. 31. http://archive.ics.uci.edu/ml/datasets/Hepatitis, last accessed: 20 March 2013.
• 32. Wilamowski BM, Yu H. Improved Computation for Levenberg-Marquardt Training IEEE Trans Neural Networks 2010;21:930-937.
• 33. Watkins A. AIRS: A resource limited artificial immune classifier. Master Thesis, Mississippi State University, 2001.
• 34. Myers DJ, Hutchinson RA. Efficient implementation of piecewise linear activation function for digital VLSI neural Networks. Electronics Letters 1989;25:1662-1663.
• 35. Bharkhada BK. Efficient FPGA implementation of a generic function approximator and its application to neural net computation. Master Thesis, University of Cincinnati, 2003.
• 36. Nordström T, Svensson B. Using and designing massively parallel computers for artificial neural networks. Journal of Parallel and Distributed Computing 1992;14:260 285.
• 37. Amin H, Curtis KM, Hayes-Gill BR. Piecewise linear approximation applied to nonlinear function of a neural network. IEE Proceedings-Circuits Devices and Systems 1997;144:313-317.
• 38. Tommiska MT. Efficient digital implementation of the sigmoid function for reprogrammable logic. IEE ProceedingsComputers and Digital Techniques 2003;150:403-411.
• 39. 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.
• 40. Temurtas H, Yumusak N, Temurtas F. A comparative study on diabetes disease diagnosis using neural networks. Expert Syst Applicat 2009;36:8610-8615.