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

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.

[1]  Alexander J. Smola,et al.  Learning with Kernels: support vector machines, regularization, optimization, and beyond , 2001, Adaptive computation and machine learning series.

[2]  Arif Gülten,et al.  Genetic algorithm wrapped Bayesian network feature selection applied to differential diagnosis of erythemato-squamous diseases , 2013, Digit. Signal Process..

[3]  William G. Baxt,et al.  Use of an Artificial Neural Network for Data Analysis in Clinical Decision-Making: The Diagnosis of Acute Coronary Occlusion , 1990, Neural Computation.

[4]  Elif Derya Übeyli,et al.  Automatic Detection of Erythemato-Squamous Diseases Using k-Means Clustering , 2010, Journal of Medical Systems.

[5]  Patrick Flandrin,et al.  Wavelet analysis and synthesis of fractional Brownian motion , 1992, IEEE Trans. Inf. Theory.

[6]  A. Izenman.  Linear Discriminant Analysis , 2013 .

[7]  Kemal Polat,et al.  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 , 2006, Digit. Signal Process..

[8]  Edward I. Altman,et al.  Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience) , 1994 .

[9]  H. Ko,et al.  Dermoscopy can be useful in differentiating scalp psoriasis from seborrhoeic dermatitis , 2011, The British journal of dermatology.

[10]  B. Scholkopf,et al.  Fisher discriminant analysis with kernels , 1999, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468).

[11]  M. Victor Wickerhauser,et al.  Adapted wavelet analysis from theory to software , 1994 .

[12]  Ahmet Sertbas,et al.  Automated lesion detection in panoramic dental radiographs , 2015, Oral Radiology.

[13]  Robert Elic,et al.  Effect of Salicylic Acid on the Activity of Betamethasone-17,21-Dipropionate in the Treatment of Erythematous Squamous Dermatoses , 1983, The Journal of international medical research.

[14]  Davar Giveki,et al.  Automatic detection of erythemato-squamous diseases using PSO-SVM based on association rules , 2013, Eng. Appl. Artif. Intell..

[15]  Yu Zhang,et al.  Rank Determination of Mental Functions by 1D Wavelets and Partial Correlation , 2016, Journal of Medical Systems.

[16]  C Beauvillain,et al.  Guidelines for the diagnosis and treatment of cutaneous squamous cell carcinoma and precursor lesions , 2011, Journal of the European Academy of Dermatology and Venereology : JEADV.

[17]  Ugur Ayan,et al.  The Differential Diagnosis of Multiple Sclerosis Using Convex Combination of Infinite Kernels. , 2017, CNS & neurological disorders drug targets.

[18]  Majaz Moonis,et al.  Stroke Subtype Clustering by Multifractal Bayesian Denoising with Fuzzy C Means and K-Means Algorithms , 2018, Complex..

[19]  Yeliz Karaca,et al.  Application and comparison of ANN and SVM for diagnostic classification for cognitive functioning , 2016 .

[20]  Marti A. Hearst.  Trends & Controversies: Support Vector Machines , 1998, IEEE Intell. Syst..

[21]  Paul Geladi,et al.  Principal Component Analysis , 1987, Comprehensive Chemometrics.

[22]  Kemal Polat,et al.  A novel hybrid intelligent method based on C4.5 decision tree classifier and one-against-all approach for multi-class classification problems , 2009, Expert Syst. Appl..

[23]  L Krain,et al.  Dermatomyositis in six patients without initial muscle involvement. , 1975, Archives of dermatology.

[24]  David E. Goldberg,et al.  Genetic algorithms and Machine Learning , 1988, Machine Learning.

[25]  W A Griffiths,et al.  Pityriasis rubra pilaris * , 1980, Clinical and experimental dermatology.

[26]  I. Jolliffe.  Principal Component Analysis and Factor Analysis , 1986 .

[27]  Alessandro Borghi,et al.  Cutaneous Manifestations in Patients With Inflammatory Bowel Diseases: Pathophysiology, Clinical Features, and Therapy , 2014, Inflammatory bowel diseases.

[28]  H. Altay Güvenir,et al.  Learning differential diagnosis of erythemato-squamous diseases using voting feature intervals , 1998, Artif. Intell. Medicine.

[29]  Juanying Xie,et al.  Using support vector machines with a novel hybrid feature selection method for diagnosis of erythemato-squamous diseases , 2011, Expert Syst. Appl..

[30]  Peter Elsner,et al.  Atypical manifestations of tinea corporis , 2007, Mycoses.

[31]  Elif Derya Übeyli,et al.  Automatic detection of erythemato-squamous diseases using adaptive neuro-fuzzy inference systems , 2004, Comput. Biol. Medicine.