International Journal of Computational and Electronics Aspects in Engineering

Open Access Open Access  Restricted Access Subscription Access

Statistical Neural Networks in the Classification of Alcoholic Liver Disease and Nonalcoholic Fatty Liver Disease


Affiliations
1 Universiti Sains Malaysia School of Mathematical Sciences, Pulau Pinang, Malaysia.
2 Universiti Sains Malaysia School of Mathematical Sciences, Pulau Pinang, Malaysia
 

This paper deals with the performance of statistical neural network in the classification of alcoholic liver disease (ALD) data and nonalcoholic fatty liver disease data (NAFLD). The study involved 73 individuals that were clinically diagnosed of alcoholic liver disease (ALD) and 80 individuals who were clinically diagnosed of nonalcoholic fatty liver disease (NAFLD). Four different neural network structure, multi-layer perceptron, radial basis function, probabilistic neural network and generalized regression neural network were applied to the data to determine the performance of statistical neural networks in the classification of liver disease data. The overall result indicates that the most suitable statistical neural network model for classifying ALD and NAFLD data is the probabilistic neural network (PNN) with a 95.7% classification performance and 67 correct classifications. Radial basis function network (RBF) and multilayer perceptron network (MLP) has the lowest classification accuracy with 55 classified samples each. The generalized regression neural network (GRNN) was the second-best network with 62 correct classifications. The computer simulation was carried out by using MATLAB 6.0 Neural Network Toolbox.
User
Notifications
Font Size

  • Ugiagbe, E. E., & Udoh, M. O., “The histopathological pattern of liver biopsies at the University of Benin Teaching Hospital”, Nigerian journal of clinical practice, 16(4), 2013.

  • Vanni, E., Bugianesi, E., Kotronen, A., De Minicis, S., Yki-Järvinen, H., & Svegliati-Baroni, G., “From the metabolic syndrome to NAFLD or< i> vice versa”, Digestive and liver Disease, 42(5), 320-330, 2010.

  • O'Shea, R. S., Dasarathy, S., & McCullough, A. J. “Alcoholic liver disease”. Hepatology, 51(1), 307-328, 2010.

  • Rajakarunakaran, S., Venkumar, P., Devaraj, D., & Rao, K. “Artificial neural network approach for fault detection in rotary system”. Applied Soft Computing, 8(1), 740-748, 2008.

  • Pye, C. J., & Bangham, J. A. “Using a genetic algorithm to adapt 1d nonlinear matched sieves for pattern classification in images”. In IS&T/SPIE's Symposium on Electronic Imaging: Science & Technology (pp. 48-55), 1995. International Society for Optics and Photonics.

  • Meech, J. A., Kawazoe, Y., Kumar, V., & Maguire, J. F. (Eds.). Intelligence in a Small Materials World. DEStech Publications, Inc, 2005.

  • Sivanandam, S. N., & Deepa, S. N. Introduction to neural networks using Matlab 6.0. Tata McGraw-Hill Education, 2006.

  • Wu, S. G., Bao, F. S., Xu, E. Y., Wang, Y. X., Chang, Y. F., & Xiang, Q. L., “A leaf recognition algorithm for plant classification using probabilistic neural network”, In Signal Processing and Information Technology, 2007 IEEE International Symposium on (pp. 11-16), 2007. IEEE.

  • Kiyan, T., & Yildirim, T. “Breast cancer diagnosis using statistical neural networks”, IU-Journal of Electrical & Electronics Engineering, 4(2), 1149-1153, 2011.

  • Kubat, M., “Neural networks: a comprehensive foundation by Simon Haykin”, Macmillan, 1994, ISBN 0-02-352781-7, 1999.

  • Specht, D. F. Probabilistic neural networks. Neural networks, 3(1), 109-118, 1990.

  • Setiono, R. “Generating concise and accurate classification rules for breast cancer diagnosis”. Artificial Intelligence in medicine, 18(3),205-219, 2000.


Abstract Views: 194

PDF Views: 86




  • Statistical Neural Networks in the Classification of Alcoholic Liver Disease and Nonalcoholic Fatty Liver Disease

Abstract Views: 194  |  PDF Views: 86

Authors

Pwasong A. Davou
Universiti Sains Malaysia School of Mathematical Sciences, Pulau Pinang, Malaysia.
Saratha Sathasivam
Universiti Sains Malaysia School of Mathematical Sciences, Pulau Pinang, Malaysia

Abstract


This paper deals with the performance of statistical neural network in the classification of alcoholic liver disease (ALD) data and nonalcoholic fatty liver disease data (NAFLD). The study involved 73 individuals that were clinically diagnosed of alcoholic liver disease (ALD) and 80 individuals who were clinically diagnosed of nonalcoholic fatty liver disease (NAFLD). Four different neural network structure, multi-layer perceptron, radial basis function, probabilistic neural network and generalized regression neural network were applied to the data to determine the performance of statistical neural networks in the classification of liver disease data. The overall result indicates that the most suitable statistical neural network model for classifying ALD and NAFLD data is the probabilistic neural network (PNN) with a 95.7% classification performance and 67 correct classifications. Radial basis function network (RBF) and multilayer perceptron network (MLP) has the lowest classification accuracy with 55 classified samples each. The generalized regression neural network (GRNN) was the second-best network with 62 correct classifications. The computer simulation was carried out by using MATLAB 6.0 Neural Network Toolbox.

References