A Novel Diagnostic Approach Based on Support Vector Machine with Linear Kernel for Classifying the Erythemato-Squamous Disease

The diagnosis of the arythema disease is a real difficulty in dermatology. It causes redness induced in the lower level of the skin by hyperemia of the capillaries. It can harm several skin damages, inflammations. In this paper, we have put our efforts to design a diagnostic approach based on Support Vector Machine (SVM) with linear kernel by classifying the erythemato-squamous disease. SVM being a large margin classifier is a powerful pattern recognition and machine learning methodology that is widely used for both linear and non-linear classification problems. Comparing testing on different kernel methods, we have noticed that our method gives the better accuracy. Choosing the optimal value of the parameters is a crucial criterion and this was achieved by performing 3 fold cross-validations.

[1]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[2]  Kurt Hornik,et al.  The support vector machine under test , 2003, Neurocomputing.

[3]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[4]  Thomas G. Dietterich.  Multiple Classifier Systems , 2000, Lecture Notes in Computer Science.

[5]  K. Chou,et al.  Using LogitBoost classifier to predict protein structural classes. , 2006, Journal of theoretical biology.

[6]  Elif Derya Übeyli.  Combined neural networks for diagnosis of erythemato-squamous diseases , 2009, Expert Syst. Appl..

[7]  Saso Dzeroski,et al.  Ensembles of Multi-Objective Decision Trees , 2007, ECML.

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

[9]  Bernhard Schölkopf,et al.  Improving the Accuracy and Speed of Support Vector Machines , 1996, NIPS.

[10]  H. A. Güvenira,et al.  An expert system for the differential diagnosis of erythemato-squamous diseases , 1999 .

[11]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

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

[13]  Lior Rokach,et al.  Ensemble-based classifiers , 2010, Artificial Intelligence Review.

[14]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[15]  M. Cevdet Ince,et al.  A new feature selection method based on association rules for diagnosis of erythemato-squamous diseases , 2009, Expert Syst. Appl..

[16]  Elif Derya íbeyli.  Combined neural networks for diagnosis of erythemato-squamous diseases , 2009 .

[17]  Mattias Ohlsson,et al.  WeAidU - a decision support system for myocardial perfusion images using artificial neural networks , 2004, Artif. Intell. Medicine.

[18]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[19]  Vivian West,et al.  Computing, Artificial Intelligence and Information Technology Ensemble strategies for a medical diagnostic decision support system: A breast cancer diagnosis application , 2005 .

[20]  Antony Browne,et al.  Neural network ensembles: combining multiple models for enhanced performance using a multistage approach , 2004, Expert Syst. J. Knowl. Eng..