Data
hepatitis

hepatitis

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Author: Source: Unknown - Please cite: 1. Title: Hepatitis Domain 2. Sources: (a) unknown (b) Donor: G.Gong (Carnegie-Mellon University) via Bojan Cestnik Jozef Stefan Institute Jamova 39 61000 Ljubljana Yugoslavia (tel.: (38)(+61) 214-399 ext.287) } (c) Date: November, 1988 3. Past Usage: 1. Diaconis,P. & Efron,B. (1983). Computer-Intensive Methods in Statistics. Scientific American, Volume 248. -- Gail Gong reported a 80% classfication accuracy 2. Cestnik,G., Konenenko,I, & Bratko,I. (1987). Assistant-86: A Knowledge-Elicitation Tool for Sophisticated Users. In I.Bratko & N.Lavrac (Eds.) Progress in Machine Learning, 31-45, Sigma Press. -- Assistant-86: 83% accuracy 4. Relevant Information: Please ask Gail Gong for further information on this database. 5. Number of Instances: 155 6. Number of Attributes: 20 (including the class attribute) 7. Attribute information: 1. Class: DIE, LIVE 2. AGE: 10, 20, 30, 40, 50, 60, 70, 80 3. SEX: male, female 4. STEROID: no, yes 5. ANTIVIRALS: no, yes 6. FATIGUE: no, yes 7. MALAISE: no, yes 8. ANOREXIA: no, yes 9. LIVER BIG: no, yes 10. LIVER FIRM: no, yes 11. SPLEEN PALPABLE: no, yes 12. SPIDERS: no, yes 13. ASCITES: no, yes 14. VARICES: no, yes 15. BILIRUBIN: 0.39, 0.80, 1.20, 2.00, 3.00, 4.00 -- see the note below 16. ALK PHOSPHATE: 33, 80, 120, 160, 200, 250 17. SGOT: 13, 100, 200, 300, 400, 500, 18. ALBUMIN: 2.1, 3.0, 3.8, 4.5, 5.0, 6.0 19. PROTIME: 10, 20, 30, 40, 50, 60, 70, 80, 90 20. HISTOLOGY: no, yes The BILIRUBIN attribute appears to be continuously-valued. I checked this with the donater, Bojan Cestnik, who replied: About the hepatitis database and BILIRUBIN problem I would like to say the following: BILIRUBIN is continuous attribute (= the number of it's "values" in the ASDOHEPA.DAT file is negative!!!); "values" are quoted because when speaking about the continuous attribute there is no such thing as all possible values. However, they represent so called "boundary" values; according to these "boundary" values the attribute can be discretized. At the same time, because of the continious attribute, one can perform some other test since the continuous information is preserved. I hope that these lines have at least roughly answered your question. 8. Missing Attribute Values: (indicated by "?") Attribute Number: Number of Missing Values: 1: 0 2: 0 3: 0 4: 1 5: 0 6: 1 7: 1 8: 1 9: 10 10: 11 11: 5 12: 5 13: 5 14: 5 15: 6 16: 29 17: 4 18: 16 19: 67 20: 0 9. Class Distribution: DIE: 32 LIVE: 123 Relabeled values in attribute SEX From: 2 To: male From: 1 To: female Relabeled values in attribute STEROID From: 1 To: no From: 2 To: yes Relabeled values in attribute ANTIVIRALS From: 2 To: no From: 1 To: yes Relabeled values in attribute FATIGUE From: 2 To: no From: 1 To: yes Relabeled values in attribute MALAISE From: 2 To: no From: 1 To: yes Relabeled values in attribute ANOREXIA From: 2 To: no From: 1 To: yes Relabeled values in attribute LIVER_BIG From: 1 To: no From: 2 To: yes Relabeled values in attribute LIVER_FIRM From: 2 To: no From: 1 To: yes Relabeled values in attribute SPLEEN_PALPABLE From: 2 To: no From: 1 To: yes Relabeled values in attribute SPIDERS From: 2 To: no From: 1 To: yes Relabeled values in attribute ASCITES From: 2 To: no From: 1 To: yes Relabeled values in attribute VARICES From: 2 To: no From: 1 To: yes Relabeled values in attribute HISTOLOGY From: 1 To: no From: 2 To: yes

20 features

Class (target)nominal2 unique values
0 missing
AGEnumeric49 unique values
0 missing
SEXnominal2 unique values
0 missing
STEROIDnominal2 unique values
1 missing
ANTIVIRALSnominal2 unique values
0 missing
FATIGUEnominal2 unique values
1 missing
MALAISEnominal2 unique values
1 missing
ANOREXIAnominal2 unique values
1 missing
LIVER_BIGnominal2 unique values
10 missing
LIVER_FIRMnominal2 unique values
11 missing
SPLEEN_PALPABLEnominal2 unique values
5 missing
SPIDERSnominal2 unique values
5 missing
ASCITESnominal2 unique values
5 missing
VARICESnominal2 unique values
5 missing
BILIRUBINnumeric34 unique values
6 missing
ALK_PHOSPHATEnumeric83 unique values
29 missing
SGOTnumeric84 unique values
4 missing
ALBUMINnumeric29 unique values
16 missing
PROTIMEnumeric44 unique values
67 missing
HISTOLOGYnominal2 unique values
0 missing

107 properties

155
Number of instances (rows) of the dataset.
20
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
167
Number of missing values in the dataset.
75
Number of instances with at least one value missing.
6
Number of numeric attributes.
14
Number of nominal attributes.
51.53
Second quartile (Median) of means among attributes of the numeric type.
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.69
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
1
Maximum entropy among attributes.
-0.53
Minimum kurtosis among attributes of the numeric type.
0.04
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.21
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.21
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
14.02
Maximum kurtosis among attributes of the numeric type.
1.43
Minimum of means among attributes of the numeric type.
0.86
Second quartile (Median) of skewness among attributes of the numeric type.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.27
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
105.33
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
70
Percentage of binary attributes.
17.72
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.73
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.13
Number of attributes divided by the number of instances.
0.13
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
48.39
Percentage of instances having missing values.
0.96
Third quartile of entropy among attributes.
0.17
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
14.2
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
2
The maximum number of distinct values among attributes of the nominal type.
-0.12
Minimum skewness among attributes of the numeric type.
5.39
Percentage of missing values.
11.18
Third quartile of kurtosis among attributes of the numeric type.
0.66
Average class difference between consecutive instances.
0.45
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.69
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
3.18
Maximum skewness among attributes of the numeric type.
0.65
Minimum standard deviation of attributes of the numeric type.
30
Percentage of numeric attributes.
90.75
Third quartile of means among attributes of the numeric type.
0.72
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
0.73
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.19
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
89.65
Maximum standard deviation of attributes of the numeric type.
20.65
Percentage of instances belonging to the least frequent class.
70
Percentage of nominal attributes.
0.09
Third quartile of mutual information between the nominal attributes and the target attribute.
0.18
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
0.17
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.27
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.78
Average entropy of the attributes.
32
Number of instances belonging to the least frequent class.
0.82
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.59
First quartile of entropy among attributes.
2.96
Third quartile of skewness among attributes of the numeric type.
0.34
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
0.45
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.69
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
4.49
Mean kurtosis among attributes of the numeric type.
0.16
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.21
First quartile of kurtosis among attributes of the numeric type.
61.04
Third quartile of standard deviation of attributes of the numeric type.
0.72
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
0.73
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.19
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
49.92
Mean of means among attributes of the numeric type.
0.53
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
3.22
First quartile of means among attributes of the numeric type.
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.18
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
0.17
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.27
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.05
Average mutual information between the nominal attributes and the target attribute.
14
Number of binary attributes.
0.01
First quartile of mutual information between the nominal attributes and the target attribute.
0.21
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.34
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
0.45
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.69
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
14.11
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
-0.01
First quartile of skewness among attributes of the numeric type.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.72
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
0
Standard deviation of the number of distinct values among attributes of the nominal type.
0.19
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
2
Average number of distinct values among the attributes of the nominal type.
1.07
First quartile of standard deviation of attributes of the numeric type.
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.18
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
0.73
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.27
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
1.28
Mean skewness among attributes of the numeric type.
0.77
Second quartile (Median) of entropy among attributes.
0.21
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.34
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
0.19
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
79.35
Percentage of instances belonging to the most frequent class.
29.74
Mean standard deviation of attributes of the numeric type.
1.66
Second quartile (Median) of kurtosis among attributes of the numeric type.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.73
Entropy of the target attribute values.
0.42
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
123
Number of instances belonging to the most frequent class.
0.48
Minimal entropy among attributes.

11 tasks

790 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
375 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Class
364 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
208 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
32 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: Leave one out - evaluation_measure: predictive_accuracy - target_feature: Class
213 runs - estimation_procedure: 10 times 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: Class
84 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: Class
24 runs - estimation_procedure: Interleaved Test then Train - target_feature: Class
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