Dermatology

Donated on 12/31/1997

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

Dataset Characteristics

Multivariate

Subject Area

Health and Medicine

Associated Tasks

Classification

Feature Type

Categorical, Integer

# Instances

366

# Features

34

Dataset Information

Additional 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.

Has Missing Values?

Yes

Introductory Paper

Learning differential diagnosis of erythemato-squamous diseases using voting feature intervals

By H. Altay Güvenir, G. Demiröz, N. Ilter. 1998

Published in Artif. Intell. Medicine

Variables Table

Variable NameRoleTypeDemographicDescriptionUnitsMissing Values
erythemaFeatureIntegerno
scalingFeatureIntegerno
definite-bordersFeatureIntegerno
itchingFeatureIntegerno
koebner phenomenonFeatureIntegerno
polygonal papulesFeatureIntegerno
follicular papulesFeatureIntegerno
oral-mucosal involvementFeatureIntegerno
knee elbow involvementFeatureIntegerno
scalp involvementFeatureIntegerno

0 to 10 of 35

Additional Variable 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

Class Labels

Class code: Class: Number of instances: 1 psoriasis 112 2 seboreic dermatitis 61 3 lichen planus 72 4 pityriasis rosea 49 5 cronic dermatitis 52 6 pityriasis rubra pilaris 20

Papers Citing this Dataset

Bidirectional Inference Networks: A Class of Deep Bayesian Networks for Health Profiling

By Hao Wang, Chengzhi Mao, Hao He, Mingmin Zhao, Tommi Jaakkola, Dina Katabi. 2019

Published in ArXiv.

Optimization on the Complementation Procedure Towards Efficient Implementation of the Index Generation Function

By Grzegorz Borowik. 2018

Published in Applied Mathematics and Computer Science.

K-groups: A Generalization of K-means Clustering

By Songzi Li, Maria Rizzo. 2017

Published in

Kernel k-Groups via Hartigan's Method

By Guilherme Francca, Maria Rizzo, Joshua Vogelstein. 2017

Published in

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14 citations
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Creators

Nilsel Ilter

H. Guvenir

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