Data
hepatitis

hepatitis

active ARFF Publicly available Visibility: public Uploaded 06-04-2014 by Jan van Rijn
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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

19 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.
32
Number of instances belonging to the least frequent class.
14
Number of binary attributes.
70
Percentage of binary attributes.
48.39
Percentage of instances having missing values.
0.66
Average class difference between consecutive instances.
5.39
Percentage of missing values.
0.13
Number of attributes divided by the number of instances.
30
Percentage of numeric attributes.
79.35
Percentage of instances belonging to the most frequent class.
70
Percentage of nominal attributes.
123
Number of instances belonging to the most frequent class.
20.65
Percentage of instances belonging to the least frequent class.

11 tasks

808 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
376 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Class
365 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
209 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
50 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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