A New Method for Diagnosing Erythemato-squamous Diseases Based on Virtual Coding and Multinomial Logistic Regression Penalized via Elastic Net_Journal of Biomedical Engineering

Authors：

 WANGJinjia , LIHui

College of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China;

Corresponding author：

WANGJinjia, Email: wjj@ysu.edu.cn

Keywords：

erythemato-squamous diseases; multinomial logistic regression; elastic net; coordinate descent; virtual coding

DOI：

10.7507/1001-5515.20150138

Video：

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Erythemato-squamous diseases are a general designation of six common skin diseases, of which the differential diagnosis is a difficult problem in dermatology. This paper presents a new method based on virtual coding for qualitative variables and multinomial logistic regression penalized via elastic net. Considering the attributes of variables, a virtual coding is applied and contributes to avoid the irrationality of calculating nominal values directly. Multinomial logistic regression model penalized via elastic net is thence used to fit the correlation between the features and classification of diseases. At last, parameter estimations can be attained through coordinate descent. This method reached accuracy rate of 98.34%±0.0027% using 10-fold cross validation in the experiments. Our method attained equivalent accuracy rate compared to the results of other methods, but steps are simpler and stability is higher.

Citation： WANGJinjia, LIHui. A New Method for Diagnosing Erythemato-squamous Diseases Based on Virtual Coding and Multinomial Logistic Regression Penalized via Elastic Net. Journal of Biomedical Engineering, 2015, 32(4): 757-762. doi: 10.7507/1001-5515.20150138 Copy

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16.	OZCIFT A,GULTEN A.Genetic algorithm wrapped Bayesian network feature selection applied to differential diagnosis of erythemato-squamous diseases[J].Digital Signal Processing,2013,23(1):230-237.
17.	ABDI M J,GIVEKI D.Automatic detection of erythemato-squamous diseases using PSO-SVM based on association rules[J].Engineering Application of Artificial Intelligence,2013,26(1):603-608.
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1. GUVENIR H A,EMEKSIZ N.An expert system for the differential diagnosis of erythemato-squamous diseases[J].Expert Systems with Applications,2000,18(1):43-49.
2. GUVENIR H A,DEMIROZ G,ILTER N.Learning differential diagnosis of erythemato-squamous diseases using voting feature intervals[J].Artificial Intelligence in Medicine,1998,13(3):147-165.
3. UBEYLI E D,GULER I.Automatic detection of erythemato-squamous diseases using adaptive neuro-fuzzy inference systems[J].Computers in Biology and Medicine,2005,35(5):421-433.
4. UBEYLI E D.Multiclass support vector machines for diagnosis of erythemato-squamous diseases[J].Expert Systems with Applications,2008,35(4):1733-1740.
5. UBEYLI E D.Combined neural networks for diagnosis of erythemato-squamous diseases[J].Expert Systems with Applications,2009,36(3):5107-5112.
6. LUUKKA P,LEPPALAMPI T.Similarity classifier with generalized mean applied to medical data[J].Computers in Biology and Medicine,2006,36(9):1026-1040.
7. LUUKKA P.Similarity classifier using similarity measure derived from Yu's norms in classification of medical data sets[J].Computers in Biology and Medicine,2007,37(8):1133-1140.
8. POLAT K,GUNES 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[J].Digital Signal Processing,2006,16(6):922-930.
9. POLAT K,GUNES S.A novel hybrid intelligent method based on C4.5 decision tree classifier and one-against-all[J].Expert Systems with Applications,2009,36(2):1578-1592.
10. UBEYLI E D,DOGDU E.Automatic detection of erythemato-squamous diseases using k-means clustering[J].Journal of Medical Systems,2010,34(2):179-184.
11. NANNI L.An ensemble of classifiers for the diagnosis of erythemato-squamous diseases[J].Neurocomputing,2006,69(7-9):842-845.
12. LIU H W,SUN J G,LIU L,et al.Feature selection with dynamic mutual information[J].Pattern Recognition,2009,42(7):1330-1339.
13. KARABATAK M,INCE M C.A new feature selection method based on association rules for diagnosis of erythemato-squamous diseases[J].Expert Systems with Applications,2009,36(10):12500-12505.
14. XIE J Y,WANG C X.Using support vector machines with a novel hybrid feature selection method for diagnosis of erythemato-squamous diseases[J].Expert Systems with Applications,2011,38(5):5809-5815.
15. OZCIFT A,GULTEN A.A robust multiclass feature selection strategy based on rotation forest ensemble algorithm for diagnosis of erythemato-squamous diseases[J].Journal of Medical Systems,2012,36(2):941-949.
16. OZCIFT A,GULTEN A.Genetic algorithm wrapped Bayesian network feature selection applied to differential diagnosis of erythemato-squamous diseases[J].Digital Signal Processing,2013,23(1):230-237.
17. ABDI M J,GIVEKI D.Automatic detection of erythemato-squamous diseases using PSO-SVM based on association rules[J].Engineering Application of Artificial Intelligence,2013,26(1):603-608.
18. TAN P,STEINBACH M,KUMAR V.Introduction to data mining[M].Addison Wesley,2005:33-41.
19. ZOU H,HASTIE T.Regularization and variable selection via the elastic net[J].Journal of the Royal Statistical Society,2005,67(2):301-320.
20. 陈希孺.广义线性模型(五)[J].数理统计与管理,2003,22(3):56-63.
21. FRIEDMAN J,HASTIE T,HOFLING H,et al.Pathwise coordinate optimization[J].The Annals of Applied Statistics,2007,1(2):302-332.

Journal of Biomedical Engineering

A New Method for Diagnosing Erythemato-squamous Diseases Based on Virtual Coding and Multinomial Logistic Regression Penalized via Elastic Net

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