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Dermatology Data Set
Download: Data Folder, Data Set Description

Abstract: Aim for this dataset is to determine the type of Eryhemato-Squamous Disease.

Data Set Characteristics:  


Number of Instances:




Attribute Characteristics:

Categorical, Integer

Number of Attributes:


Date Donated


Associated Tasks:


Missing Values?


Number of Web Hits:



Original Owners:

1. Nilsel Ilter, M.D., Ph.D.,
Gazi University,
School of Medicine
06510 Ankara, Turkey
Phone: +90 (312) 214 1080

2. H. Altay Guvenir, PhD.,
Bilkent University,
Department of Computer Engineering and Information Science,
06533 Ankara, Turkey
Phone: +90 (312) 266 4133
Email: guvenir '@'


H. Altay Guvenir,
Bilkent University,
Department of Computer Engineering and Information Science,
06533 Ankara, Turkey
Phone: +90 (312) 266 4133
Email: guvenir '@'

Data Set Information:

This database contains 34 attributes, 33 of which are linear valued and one of them is nominal.

The differential diagnosis of erythemato-squamous diseases is a real problem in dermatology. They all share the clinical features of erythema and scaling, with very little differences. The diseases in this group are psoriasis, seboreic dermatitis, lichen planus, pityriasis rosea, cronic dermatitis, and pityriasis rubra pilaris. Usually a biopsy is necessary for the diagnosis but unfortunately these diseases share many histopathological features as well. Another difficulty for the differential diagnosis is that a disease may show the features of another disease at the beginning stage and may have the characteristic features at the following stages. Patients were first evaluated clinically with 12 features. Afterwards, skin samples were taken for the evaluation of 22 histopathological features. The values of the histopathological features are determined by an analysis of the samples under a microscope.

In the dataset constructed for this domain, the family history feature has the value 1 if any of these diseases has been observed in the family, and 0 otherwise. The age feature simply represents the age of the patient. Every other feature (clinical and histopathological) was given a degree in the range of 0 to 3. Here, 0 indicates that the feature was not present, 3 indicates the largest amount possible, and 1, 2 indicate the relative intermediate values.

The names and id numbers of the patients were recently removed from the database.

Attribute Information:

Clinical Attributes: (take values 0, 1, 2, 3, unless otherwise indicated)
1: erythema
2: scaling
3: definite borders
4: itching
5: koebner phenomenon
6: polygonal papules
7: follicular papules
8: oral mucosal involvement
9: knee and elbow involvement
10: scalp involvement
11: family history, (0 or 1)
34: Age (linear)

Histopathological Attributes: (take values 0, 1, 2, 3)
12: melanin incontinence
13: eosinophils in the infiltrate
14: PNL infiltrate
15: fibrosis of the papillary dermis
16: exocytosis
17: acanthosis
18: hyperkeratosis
19: parakeratosis
20: clubbing of the rete ridges
21: elongation of the rete ridges
22: thinning of the suprapapillary epidermis
23: spongiform pustule
24: munro microabcess
25: focal hypergranulosis
26: disappearance of the granular layer
27: vacuolisation and damage of basal layer
28: spongiosis
29: saw-tooth appearance of retes
30: follicular horn plug
31: perifollicular parakeratosis
32: inflammatory monoluclear inflitrate
33: band-like infiltrate

Relevant Papers:

G. Demiroz, H. A. Govenir, and N. Ilter, "Learning Differential Diagnosis of Eryhemato-Squamous Diseases using Voting Feature Intervals", Aritificial Intelligence in Medicine
[Web Link]

Papers That Cite This Data Set1:

Vassilis Athitsos and Stan Sclaroff. Boosting Nearest Neighbor Classifiers for Multiclass Recognition. Boston University Computer Science Tech. Report No, 2004-006. 2004. [View Context].

Gisele L. Pappa and Alex Alves Freitas and Celso A A Kaestner. Attribute Selection with a Multi-objective Genetic Algorithm. SBIA. 2002. [View Context].

M. V. Fidelis and Heitor S. Lopes and Alex Alves Freitas. Discovering Comprehensible Classification Rules with a Genetic Algorithm. UEPG, CPD CEFET-PR, CPGEI PUC-PR, PPGIA Praa Santos Andrade, s/n Av. Sete de Setembro. [View Context].

Rafael S. Parpinelli and Heitor S. Lopes and Alex Alves Freitas. PART FOUR: ANT COLONY OPTIMIZATION AND IMMUNE SYSTEMS Chapter X An Ant Colony Algorithm for Classification Rule Discovery. CEFET-PR, Curitiba. [View Context].

Rafael S. Parpinelli and Heitor S. Lopes and Alex Alves Freitas. An Ant Colony Based System for Data Mining: Applications to Medical Data. CEFET-PR, CPGEI Av. Sete de Setembro, 3165. [View Context].

Gisele L. Pappa and Alex Alves Freitas and Celso A A Kaestner. AMultiobjective Genetic Algorithm for Attribute Selection. Computing Laboratory Pontificia Universidade Catolica do Parana University of Kent at Canterbury. [View Context].

Perry Moerland. Mixtures of latent variable models for density estimation and classification. E S E A R C H R E P R O R T I D I A P D a l l e M o l l e I n s t i t u t e f o r Pe r cep t ua l A r t i f i c i a l Intelligence . [View Context].

H. Altay Guvenir. A Classification Learning Algorithm Robust to Irrelevant Features. Bilkent University, Department of Computer Engineering and Information Science. [View Context].

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