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dermatology

dermatology

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1. Title: Dermatology Database 2. Source Information: (a) 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@cs.bilkent.edu.tr (b) Donor: H. Altay Guvenir, Bilkent University, Department of Computer Engineering and Information Science, 06533 Ankara, Turkey Phone: +90 (312) 266 4133 Email: guvenir@cs.bilkent.edu.tr (c) Date: January, 1998 3. Past Usage: 1. G. Demiroz, H. A. Govenir, and N. Ilter, "Learning Differential Diagnosis of Eryhemato-Squamous Diseases using Voting Feature Intervals", Aritificial Intelligence in Medicine, The aim is to determine the type of Eryhemato-Squamous Disease. 4. Relevant 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. 5. Number of Instances: 366 6. Number of Attributes: 34 7. Attribute Information: -- Complete attribute documentation: 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 8. Missing Attribute Values: 8 (in Age attribute). Distinguished with '?'. 9. Class Distribution: Database: Dermatology 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

35 features

class (target)nominal6 unique values
0 missing
erythemanominal4 unique values
0 missing
scalingnominal4 unique values
0 missing
definite_bordersnominal4 unique values
0 missing
itchingnominal4 unique values
0 missing
koebner_phenomenonnominal4 unique values
0 missing
polygonal_papulesnominal4 unique values
0 missing
follicular_papulesnominal4 unique values
0 missing
oral_mucosal_involvementnominal4 unique values
0 missing
knee_and_elbow_involvementnominal4 unique values
0 missing
scalp_involvementnominal4 unique values
0 missing
family_historynominal2 unique values
0 missing
melanin_incontinencenominal4 unique values
0 missing
eosinophils_in_the_infiltratenominal3 unique values
0 missing
PNL_infiltratenominal4 unique values
0 missing
fibrosis_of_the_papillary_dermisnominal4 unique values
0 missing
exocytosisnominal4 unique values
0 missing
acanthosisnominal4 unique values
0 missing
hyperkeratosisnominal4 unique values
0 missing
parakeratosisnominal4 unique values
0 missing
clubbing_of_the_rete_ridgesnominal4 unique values
0 missing
elongation_of_the_rete_ridgesnominal4 unique values
0 missing
thinning_of_the_suprapapillary_epidermisnominal4 unique values
0 missing
spongiform_pustulenominal4 unique values
0 missing
munro_microabcessnominal4 unique values
0 missing
focal_hypergranulosisnominal4 unique values
0 missing
disappearance_of_the_granular_layernominal4 unique values
0 missing
vacuolisation_and_damage_of_basal_layernominal4 unique values
0 missing
spongiosisnominal4 unique values
0 missing
saw-tooth_appearance_of_retesnominal4 unique values
0 missing
follicular_horn_plugnominal4 unique values
0 missing
perifollicular_parakeratosisnominal4 unique values
0 missing
inflammatory_monoluclear_inflitratenominal4 unique values
0 missing
band-like_infiltratenominal4 unique values
0 missing
Agenumeric60 unique values
8 missing
Show all 35 features

107 properties

NumberOfInstances
366
Number of instances (rows) of the dataset.
NumberOfFeatures
35
Number of attributes (columns) of the dataset.
NumberOfClasses
6
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
8
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
8
Number of instances with at least one value missing.
NumberOfNumericFeatures
1
Number of numeric attributes.
NumberOfSymbolicFeatures
34
Number of nominal attributes.
J48.00001.Kappa
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
MeanAttributeEntropy
1.2
Average entropy of the attributes.
MinorityClassSize
20
Number of instances belonging to the least frequent class.
PercentageOfSymbolicFeatures
97.14
Percentage of nominal attributes.
Quartile3MutualInformation
0.66
Third quartile of mutual information between the nominal attributes and the target attribute.
CfsSubsetEval_DecisionStumpErrRate
0.07
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
RandomTreeDepth2ErrRate
0.1
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
J48.0001.AUC
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
MeanKurtosisOfNumericAtts
-0.71
Mean kurtosis among attributes of the numeric type.
NaiveBayesAUC
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
Quartile1AttributeEntropy
0.93
First quartile of entropy among attributes.
Quartile3SkewnessOfNumericAtts
0.07
Third quartile of skewness among attributes of the numeric type.
CfsSubsetEval_DecisionStumpKappa
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
RandomTreeDepth2Kappa
0.87
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
J48.0001.ErrRate
0.07
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
MeanMeansOfNumericAtts
36.3
Mean of means among attributes of the numeric type.
NaiveBayesErrRate
0.04
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
Quartile1KurtosisOfNumericAtts
-0.71
First quartile of kurtosis among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
15.32
Third quartile of standard deviation of attributes of the numeric type.
CfsSubsetEval_NaiveBayesAUC
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
RandomTreeDepth3AUC
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
J48.0001.Kappa
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
MeanMutualInformation
0.45
Average mutual information between the nominal attributes and the target attribute.
NaiveBayesKappa
0.96
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
Quartile1MeansOfNumericAtts
36.3
First quartile of means among attributes of the numeric type.
REPTreeDepth1AUC
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
CfsSubsetEval_NaiveBayesErrRate
0.07
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
RandomTreeDepth3ErrRate
0.1
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.87
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
J48.001.AUC
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
MeanNoiseToSignalRatio
1.67
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
NumberOfBinaryFeatures
1
Number of binary attributes.
Quartile1MutualInformation
0.28
First quartile of mutual information between the nominal attributes and the target attribute.
REPTreeDepth1ErrRate
0.1
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
CfsSubsetEval_NaiveBayesKappa
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
StdvNominalAttDistinctValues
0.52
Standard deviation of the number of distinct values among attributes of the nominal type.
J48.001.ErrRate
0.07
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
MeanNominalAttDistinctValues
3.97
Average number of distinct values among the attributes of the nominal type.
Quartile1SkewnessOfNumericAtts
0.07
First quartile of skewness among attributes of the numeric type.
REPTreeDepth1Kappa
0.87
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
CfsSubsetEval_kNN1NAUC
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
kNN1NAUC
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
J48.001.Kappa
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MeanSkewnessOfNumericAtts
0.07
Mean skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
15.32
First quartile of standard deviation of attributes of the numeric type.
REPTreeDepth2AUC
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
CfsSubsetEval_kNN1NErrRate
0.07
Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
kNN1NErrRate
0.07
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
MajorityClassPercentage
30.6
Percentage of instances belonging to the most frequent class.
MeanStdDevOfNumericAtts
15.32
Mean standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
1.25
Second quartile (Median) of entropy among attributes.
REPTreeDepth2ErrRate
0.1
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
CfsSubsetEval_kNN1NKappa
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
kNN1NKappa
0.91
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
MajorityClassSize
112
Number of instances belonging to the most frequent class.
MinAttributeEntropy
0.39
Minimal entropy among attributes.
Quartile2KurtosisOfNumericAtts
-0.71
Second quartile (Median) of kurtosis among attributes of the numeric type.
REPTreeDepth2Kappa
0.87
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
ClassEntropy
2.43
Entropy of the target attribute values.
MaxAttributeEntropy
1.97
Maximum entropy among attributes.
MinKurtosisOfNumericAtts
-0.71
Minimum kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
36.3
Second quartile (Median) of means among attributes of the numeric type.
REPTreeDepth3AUC
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
DecisionStumpAUC
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
MaxKurtosisOfNumericAtts
-0.71
Maximum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
36.3
Minimum of means among attributes of the numeric type.
Quartile2MutualInformation
0.45
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
REPTreeDepth3ErrRate
0.1
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
DecisionStumpErrRate
0.5
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
MaxMeansOfNumericAtts
36.3
Maximum of means among attributes of the numeric type.
MinMutualInformation
0.09
Minimal mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
0.07
Second quartile (Median) of skewness among attributes of the numeric type.
REPTreeDepth3Kappa
0.87
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
DecisionStumpKappa
0.34
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
MaxMutualInformation
0.86
Maximum mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
2
The minimal number of distinct values among attributes of the nominal type.
PercentageOfBinaryFeatures
2.86
Percentage of binary attributes.
Quartile2StdDevOfNumericAtts
15.32
Second quartile (Median) of standard deviation of attributes of the numeric type.
RandomTreeDepth1AUC
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Dimensionality
0.1
Number of attributes divided by the number of instances.
MaxNominalAttDistinctValues
6
The maximum number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
0.07
Minimum skewness among attributes of the numeric type.
PercentageOfInstancesWithMissingValues
2.19
Percentage of instances having missing values.
Quartile3AttributeEntropy
1.53
Third quartile of entropy among attributes.
RandomTreeDepth1ErrRate
0.1
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
EquivalentNumberOfAtts
5.42
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
MaxSkewnessOfNumericAtts
0.07
Maximum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
15.32
Minimum standard deviation of attributes of the numeric type.
PercentageOfMissingValues
0.06
Percentage of missing values.
Quartile3KurtosisOfNumericAtts
-0.71
Third quartile of kurtosis among attributes of the numeric type.
AutoCorrelation
0.42
Average class difference between consecutive instances.
RandomTreeDepth1Kappa
0.87
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
J48.00001.AUC
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
MaxStdDevOfNumericAtts
15.32
Maximum standard deviation of attributes of the numeric type.
MinorityClassPercentage
5.46
Percentage of instances belonging to the least frequent class.
PercentageOfNumericFeatures
2.86
Percentage of numeric attributes.
Quartile3MeansOfNumericAtts
36.3
Third quartile of means among attributes of the numeric type.
CfsSubsetEval_DecisionStumpAUC
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
RandomTreeDepth2AUC
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
J48.00001.ErrRate
0.07
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Show all 107 properties

28 tasks

Supervised Classification on dermatology
673 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on dermatology
313 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on dermatology
307 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on dermatology
163 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on dermatology
31 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
Supervised Classification on dermatology
0 runs - estimation_procedure: Leave one out - evaluation_measure: predictive_accuracy - target_feature: class
Learning Curve on dermatology
171 runs - estimation_procedure: 10 times 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
Learning Curve on dermatology
73 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
Learning Curve on dermatology
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on dermatology
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on dermatology
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on dermatology
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on dermatology
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on dermatology
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on dermatology
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on dermatology
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Supervised Data Stream Classification on dermatology
25 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
Clustering on dermatology
0 runs - estimation_procedure: 50 times Clustering
Clustering on dermatology
0 runs - estimation_procedure: 50 times Clustering
Clustering on dermatology
0 runs - estimation_procedure: 50 times Clustering
Clustering on dermatology
0 runs - estimation_procedure: 50 times Clustering
Clustering on dermatology
0 runs - estimation_procedure: 50 times Clustering
Clustering on dermatology
0 runs
Clustering on dermatology
0 runs - estimation_procedure: 50 times Clustering
Clustering on dermatology
0 runs - estimation_procedure: 50 times Clustering
Clustering on dermatology
0 runs - estimation_procedure: 50 times Clustering
Clustering on dermatology
0 runs - estimation_procedure: 50 times Clustering
Clustering on dermatology
0 runs - estimation_procedure: 50 times Clustering
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