Identification of Erythemato-Squamous Skin Diseases Using Support Vector Machines and Extreme Learning Machines: A Comparative Study towards Effective Diagnosis

  • Sunday Olusanya Olatunji University of Dammam
  • Hossain Arif BRAC University
Keywords: Extreme Learning Machine, Support Vector Machine, Erythemato-squamous skin diseases


Extreme Learning Machine (ELM) is a recently introduced learning algorithm for single hidden-layer feedforward neural network. Compared with classical learning algorithms in neural network, e.g. Back Propagation, ELM can achieve better performance with much shorter learning time. In the existing literature its better performance and comparison with Support Vector Machine (SVM), over regression and general classification problems, have caught the attention of many researchers. In this work, a comparison between ELM and SVM on identification of Erythemato-Squamous skin diseases is investigated. Detailed comparative studies were carried out through adequate experimentation. Experimental results indicated that ELM outperformed SVM. The effect of varying the size of training and testing sets on the performance of classifiers was also investigated in this study. The two techniques compared proved to be viable tools in this germane field of medical diagnosis.

Author Biographies

Sunday Olusanya Olatunji, University of Dammam
Assistant Professor
Hossain Arif, BRAC University

Senior Lecturer

Department of Computer Science and Engineering


Xie, J., et al., Novel Hybrid Feature Selection Algorithms for Diagnosing Erythemato-Squamous Diseases, in Health Information Science, J. He, et al., Editors. 2012, Springer Berlin Heidelberg. p. 173-185.

Xie, J. and C. Wang, Using support vector machines with a novel hybrid feature selection method for diagnosis of erythemato-squamous diseases. Expert Systems with Applications, 2011. 38(5): p. 5809-5815.

Vojislav Kecman and Mirna Kikec, Erythemato-squamous diseases diagnosis by support vector machines and RBF NN”, Proceedings of the 10th International Conference on Artificial intelligence and Soft Computing: Part I, pp. 613-620, 2010, in Springer -Verlag Berlin Heidelberg 2010, e.a. L. Tutkowski, Editor. 2010. p. 613-620.

Übeylı, E.D. and İ. Güler, Automatic detection of erythemato-squamous diseases using adaptive neuro-fuzzy inference systems. Computers in Biology and Medicine, 2005. 35(5): p. 421-433.

Huang, G.B., Q.Y. Zhu, and C.K. Siew, Extreme learning machine: a new learning scheme of feedforward neural networks, in International Joint Conference on Neural Networks (IJCNN2004). 2004: Budapest, Hungary. p. 985 - 990.

Liu, H., J. Li, and L. Wong, Use of Extreme Patient Samples for Outcome Prediction from Gene Expression Data. Bioinformatics, 2005. 21(16): p. 3377-3384.

Mahmoud, S.A. and S.O. Olatunji, Automatic Recognition of Off-line Handwritten Arabic (Indian) Numerals Using Support Vector and Extreme Learning Machines. International Journal of Imaging, 2009. 2(A09): p. 34-53.

Olatunji, S.O., Comparison of Extreme Learning Machines and Support Vector Machines on Premium and Regular Gasoline Classification for Arson and Oil Spill Investigation. Asian Journal Of Engineering, Sciences & Technology, 2011. 1(1): p. 1-7.

Olatunji, S.O., I.A. Adeleke, and A. Akingbesote, Data Mining Based on Extreme Learning Machines for the Classification of Premium and Regular Gasoline in Arson and Fuel Spill Investigation. Journal Of Computing, 2011. 3(3): p. 130-136.

Olatunji, S.O., S. Ali, and A. Abdul Azeez. Modeling Permeability Prediction Using Extreme Learning Machines. in Fourth Asia International Conference on Mathematical/Analytical Modelling and Computer Simulation, AMS2010. 2010. Kota- Kinabalu, Malaysia: IEEE.

Olatunji, S.O., et al., Extreme Learning Machine as Maintainability Prediction model for Object-Oriented Software Systems. Journal of Computing, Volume 2, Issue 8, August 2010, 2010. 2(8): p. 42-56.

Teddy Mantoro, et al., Extreme learning machine for user location prediction in mobile environment. International Journal of Pervasive Computing and Communications, 2011. 7(2): p. 162 - 180.

Duda, R.O., P.E. Hart, and D.G. Stock, Pattern Classification. 2001, New York: John Wiley and Sons. 654.

Huang, G.B., et al., Can threshold networks be trained directly? IEEE Trans. Circuits Syst. II, 2006. 53(3): p. 187-191.

Huang, G.B., Q.Y. Zhu, and C.K. Siew, Extreme learning machine: Theory and applications. Neurocomputing, Elsevier, 2006. 70(1-3): p. 489-501.

Huang, G.B. and H.A. Babri, Feedforward neural networks with arbitrary bounded nonlinear activation functions. 9(1):224–229. IEEE Trans Neural Network, 1998. 9(1): p. 224-229.