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Classification of Erythematous - Squamous Skin Diseases Through SVM Kernels and Identification of Features with 1-D Continuous Wavelet Coefficient

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Computational Science and Its Applications – ICCSA 2018 (ICCSA 2018)

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Abstract

Feature extraction is a kind of dimensionality reduction which refers to the differentiating features of a dataset. In this study, we have worked on ESD_Data Set (33 attributes), composed of clinical and histopathological attributes of erythematous-squamous skin diseases (ESDs) (psoriasis, seborrheic dermatitis, lichen planus, pityriasis rosea, chronic dermatitis, pityriasis rubra pilaris). It’s aimed to obtain distinguishing significant attributes in ESD_Data Set for a successful classification of ESDs. We have focused on three areas: (a) By applying 1-D continuous wavelet coefficient analysis, Principle Component Analysis and Linear Discriminant Analysis to ESD_Data Set; w_ESD Data Set, p_ESD Data Set and l_ESD Data Set were formed. (b) By applying Support Vector Machine kernel algorithms (Linear, Quadratic, Cubic, Gaussian) to these datasets, accuracy rates were obtained. (c) w_ESD Data Set had the highest accuracy. This study seeks to identify deficiencies in literature to determine the distinguishing significant attributes in ESD_Data Set to classify ESDs.

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References

  1. Goldberg, D.E., Holland, J.H.: Genetic algorithms and machine learning. Mach. Learn. 3(2), 95–99 (1988)

    Article  Google Scholar 

  2. Birdal, R.G., Gümüş, E., Sertbaş, A., Birdal, I.S.: Automated lesion detection in panoramic dental radiographs. Oral Radiol. 32(2), 111–118 (2016)

    Article  Google Scholar 

  3. Karaca, Y., Cattani, C., Moonis, M., Bayrak, Ş.: Stroke subtype clustering by multifractal bayesian denoising with Fuzzy C Means and K-means algorithms. Complexity 2018, 1–15 (2018)

    Article  Google Scholar 

  4. Griffiths, W.A.D.: Pityriasis rubra pilaris. Clin. Exp. Dermatol. 5(1), 105–112 (1980)

    Article  Google Scholar 

  5. Kim, G.W., Jung, H.J., Ko, H.C., Kim, M.B., Lee, W.J., Lee, S.J., Kim, D.W., Kim, B.S.: Dermoscopy can be useful in differentiating scalp psoriasis from seborrhoeic dermatitis. Br. J. Dermatol. 164(3), 652–656 (2011)

    Google Scholar 

  6. Elic, R., Durocher, L.P., Kavalec, E.C.: Effect of salicylic acid on the activity of betamethasone-17, 21-dipropionate in the treatment of erythematous squamous dermatoses. J. Int. Med. Res. 11(2), 108–112 (1983)

    Article  Google Scholar 

  7. Krain, L.S.: Dermatomyositis in six patients without initial muscle involvement. Arch. Dermatol. 111(2), 241–245 (1975)

    Article  Google Scholar 

  8. Marzano, A.V., Borghi, A., Stadnicki, A., Crosti, C., Cugno, M.: Cutaneous manifestations in patients with inflammatory bowel diseases: pathophysiology, clinical features, and therapy. Inflamm. Bowel Dis. 20(1), 213–227 (2013)

    Article  Google Scholar 

  9. Ziemer, M., Seyfarth, F., Elsner, P., Hipler, U.C.: Atypical manifestations of tinea corporis. Mycoses 50(s2), 31–35 (2007)

    Article  Google Scholar 

  10. Bonerandi, J.J., Beauvillain, C., Caquant, L., Chassagne, J.F., Chaussade, V., Clavere, P., Desouches, C., Garnier, F., Grolleau, J.L., Grossin, M., Jourdain, A.: Guidelines for the diagnosis and treatment of cutaneous squamous cell carcinoma and precursor lesions. J. Eur. Acad. Dermatol. Venereol. 25(s5), 1–51 (2011)

    Article  Google Scholar 

  11. Baxt, W.G.: Use of an artificial neural network for data analysis in clinical decision-making: the diagnosis of acute coronary occlusion. Neural Comput. 2(4), 480–489 (1990)

    Article  Google Scholar 

  12. Ubeyli, E.D., Güler, I.: Automatic detection of erythemato-squamous diseases using adaptive neuro-fuzzy inference systems. Comput. Biol. Med. 35(5), 421–433 (2005)

    Article  Google Scholar 

  13. Polat, K., Güneş, S.: A novel hybrid intelligent method based on C4. 5 decision tree classifier and one-against-all approach for multi-class classification problems. Expert Syst. Appl. 36(2), 1587–1592 (2009)

    Article  Google Scholar 

  14. Guvenir, H.A., Demiröz, G., Ilter, N.: Learning differential diagnosis of erythemato-squamous diseases using voting feature intervals. Artif. Intell. Med. 13(3), 147–165 (1998)

    Article  Google Scholar 

  15. Ubeyli, E.D., Doğdu, E.: Automatic detection of erythemato-squamous diseases using k-means clustering. J. Med. Syst. 34(2), 179–184 (2010)

    Article  Google Scholar 

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

    Article  Google Scholar 

  17. Abdi, M.J., Giveki, D.: Automatic detection of erythemato - squamous diseases using PSO - SVM based on association rules. Eng. Appl. Artif. Intell. 26(1), 603–608 (2013)

    Article  Google Scholar 

  18. Polat, K., Güneş, S.: The effect to diagnostic accuracy of decision tree classifier of fuzzy and k-NN based weighted pre-processing methods to diagnosis of erythemato-squamous diseases. Digit. Signal Proc. 16(6), 922–930 (2006)

    Article  Google Scholar 

  19. Ozcift, A., Gulten, A.: Genetic algorithm wrapped Bayesian network feature selection applied to differential diagnosis of erythemato-squamous diseases. Digit. Signal Proc. 23(1), 230–237 (2013)

    Article  MathSciNet  Google Scholar 

  20. Asuncion, A., Newman, D.: UCI machine learning repository (2007)

    Google Scholar 

  21. Wickerhauser, M.V.: Adapted Wavelet Analysis from Theory to Software. IEEE Press, New York (1994)

    MATH  Google Scholar 

  22. Karaca, Y., Aslan, Z., Cattani, C., Galletta, D., Zhang, Y.: Rank determination of mental functions by 1D wavelets and partial correlation. J. Med. Syst. 41(2), 1–10 (2017)

    Google Scholar 

  23. Flandrin, P.: Wavelet analysis and synthesis of fractional Brownian motion. IEEE Trans. Inf. Theory 38(2), 910–917 (1992)

    Article  MathSciNet  Google Scholar 

  24. Jolliffe, I. T.: Principal component analysis and factor analysis. In: Principal Component Analysis, pp. 115–128. Springer (1986)

    Chapter  Google Scholar 

  25. Wood, F., Esbensen, K., Geladi, P.: Principal component analysis. Chemometr. Intel. Lab. Syst 2(1987), 37–52 (1987)

    Google Scholar 

  26. Izenman, A.J.: Linear discriminant analysis. In: Modern Multivariate Statistical Techniques, pp. 237–280 (2013)

    Google Scholar 

  27. Mika, S., Ratsch, G., Weston, J., Scholkopf, B., Mullers, K.R.: August. Fisher discriminant analysis with kernels. In: Proceedings of the 1999 IEEE Signal Processing Society Workshop Neural Networks for Signal Processing IX, pp. 41–48 (1999)

    Google Scholar 

  28. Altman, E.I., Marco, G., Varetto, F.: Corporate distress diagnosis: comparisons using linear discriminant analysis and neural networks (the Italian experience). J. Bank. Financ. 18(3), 505–529 (1994)

    Article  Google Scholar 

  29. Hearst, M.A., Dumais, S.T., Osuna, E., Platt, J., Scholkopf, B.: Support vector machines. IEEE Intell. Syst. Appl. 13(4), 18–28 (1998)

    Article  Google Scholar 

  30. Karaca, Y., Zhang, Y., Cattani, C., Ayan, U.: The differential diagnosis of multiple sclerosis using convex combination of infinite kernels. CNS Neurol. Disord. Drug Targets (Formerly Current Drug Targets-CNS & Neurological Disorders) 16(1), 36–43 (2017)

    Article  Google Scholar 

  31. Scholkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge (2001)

    Google Scholar 

  32. Karaca, Y., Hayta, Ş.: Application and comparison of ANN and SVM for diagnostic classification for cognitive functioning. Appl. Math. Sci. 10(64), 3187–3199 (2016)

    Google Scholar 

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Authors and Affiliations

  1. University of Massachusetts Medical School, Worcester, MA, 01655, USA

    Yeliz Karaca

  2. Department of Computer Engineering, İstanbul University, 34000, İstanbul, Turkey

    Ahmet Sertbaş

  3. Department of Computer Engineering, Haliç University, 34000, İstanbul, Turkey

    Şengül Bayrak

Authors
  1. Yeliz Karaca
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  2. Ahmet Sertbaş
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  3. Şengül Bayrak
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Corresponding author

Correspondence to Yeliz Karaca .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  5. Polytechnic University of Bari, Bari, Italy

    Carmelo M. Torre

  6. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  7. Monash University, Clayton, Victoria, Australia

    David Taniar

  8. Kyushu Sangyo University, Fukuoka shi, Fukuoka, Japan

    Bernady O. Apduhan

  9. Politecnico di Bari, Bari, Italy

    Eufemia Tarantino

  10. Myongji University, Yongin, Korea (Republic of)

    Yeonseung Ryu

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Karaca, Y., Sertbaş, A., Bayrak, Ş. (2018). Classification of Erythematous - Squamous Skin Diseases Through SVM Kernels and Identification of Features with 1-D Continuous Wavelet Coefficient. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2018. ICCSA 2018. Lecture Notes in Computer Science(), vol 10961. Springer, Cham. https://doi.org/10.1007/978-3-319-95165-2_8

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  • DOI: https://doi.org/10.1007/978-3-319-95165-2_8

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  • Online ISBN: 978-3-319-95165-2

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