Data
primary-tumor_clean

primary-tumor_clean

active ARFF Publicly available Visibility: public Uploaded 05-09-2024 by Bruno Belucci Teixeira
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Author: Source: Unknown - Please cite: Citation Request: This primary tumor domain was obtained from the University Medical Centre, Institute of Oncology, Ljubljana, Yugoslavia. Thanks go to M. Zwitter and M. Soklic for providing the data. Please include this citation if you plan to use this database. 1. Title: Primary Tumor Domain 2. Sources: (a) Source: (b) Donors: Igor Kononenko, University E.Kardelj Faculty for electrical engineering Trzaska 25 61000 Ljubljana (tel.: (38)(+61) 265-161 Bojan Cestnik Jozef Stefan Institute Jamova 39 61000 Ljubljana Yugoslavia (tel.: (38)(+61) 214-399 ext.287) (c) Date: November 1988 3. Past Usage: (sveral) 1. 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: 44% accuracy 2. Clark,P. & Niblett,T. (1987). Induction in Noisy Domains. In I.Bratko & N.Lavrac (Eds.) Progress in Machine Learning, 11-30, Sigma Press. -- Simple Bayes: 48% accuracy -- CN2 (95% threshold): 45% 3. Michalski,R., Mozetic,I. Hong,J., & Lavrac,N. (1986). The Multi-Purpose Incremental Learning System AQ15 and its Testing Applications to Three Medical Domains. In Proceedings of the Fifth National Conference on Artificial Intelligence, 1041-1045. Philadelphia, PA: Morgan Kaufmann. -- Experts: 42% accuracy -- AQ15: 29-41% 4. Relevant Information: This is one of three domains provided by the Oncology Institute that has repeatedly appeared in the machine learning literature. (See also breast-cancer and lymphography.) 5. Number of Instances: 339 6. Number of Attributes: 18 including the class attribute 7. Attribute Information: (class is location of tumor) --- NOTE: All attribute values in the database have been entered as numeric values corresponding to their index in the list of attribute values for that attribute domain as given below. 1. class: lung, head & neck, esophasus, thyroid, stomach, duoden & sm.int, colon, rectum, anus, salivary glands, pancreas, gallblader, liver, kidney, bladder, testis, prostate, ovary, corpus uteri, cervix uteri, vagina, breast 2. age: <30, 30-59, >=60 3. sex: male, female 4. histologic-type: epidermoid, adeno, anaplastic 5. degree-of-diffe: well, fairly, poorly 6. bone: yes, no 7. bone-marrow: yes, no 8. lung: yes, no 9. pleura: yes, no 10. peritoneum: yes, no 11. liver: yes, no 12. brain: yes, no 13. skin: yes, no 14. neck: yes, no 15. supraclavicular: yes, no 16. axillar: yes, no 17. mediastinum: yes, no 18. abdominal: yes, no 8. Missing Attribute Values: (? indicates unknown value) Attribute#: Number of missing values 1: 0 2: 0 3: 1 4: 67 5: 155 6: 0 7: 0 8: 0 9: 0 10: 0 11: 0 12: 0 13: 1 14: 0 15: 0 16: 1 17: 0 18: 0 9. Class Distribution: Class Index: Number of instances in class: 1: 84 2: 20 3: 9 4: 14 5: 39 6: 1 7: 14 8: 6 9: 0 10: 2 11: 28 12: 16 13: 7 14: 24 15: 2 16: 1 17: 10 18: 29 19: 6 20: 2 21: 1 22: 24 Relabeled values in attribute age From: 1 To: '<30' From: 2 To: '30-59' From: 3 To: '>=60' Relabeled values in attribute sex From: 1 To: male From: 2 To: female Relabeled values in attribute histologic-type From: 1 To: epidermoid From: 2 To: adeno From: 3 To: anaplastic Relabeled values in attribute degree-of-diffe From: 1 To: well From: 2 To: fairly From: 3 To: poorly Relabeled values in attribute bone From: 1 To: yes From: 2 To: no Relabeled values in attribute bone-marrow From: 1 To: yes From: 2 To: no Relabeled values in attribute lung From: 1 To: yes From: 2 To: no Relabeled values in attribute pleura From: 1 To: yes From: 2 To: no Relabeled values in attribute peritoneum From: 1 To: yes From: 2 To: no Relabeled values in attribute liver From: 1 To: yes From: 2 To: no Relabeled values in attribute brain From: 1 To: yes From: 2 To: no Relabeled values in attribute skin From: 1 To: yes From: 2 To: no Relabeled values in attribute neck From: 1 To: yes From: 2 To: no Relabeled values in attribute supraclavicular From: 1 To: yes From: 2 To: no Relabeled values in attribute axillar From: 1 To: yes From: 2 To: no Relabeled values in attribute mediastinum From: 1 To: yes From: 2 To: no Relabeled values in attribute abdominal From: 1 To: yes From: 2 To: no Relabeled values in attribute class From: 1 To: lung From: 2 To: 'head and neck' From: 3 To: esophagus From: 4 To: thyroid From: 5 To: stomach From: 6 To: 'duoden and sm.int' From: 7 To: colon From: 8 To: rectum From: 9 To: anus From: 10 To: 'salivary glands' From: 11 To: pancreas From: 12 To: gallbladder From: 13 To: liver From: 14 To: kidney From: 15 To: bladder From: 16 To: testis From: 17 To: prostate From: 18 To: ovary From: 19 To: 'corpus uteri' From: 20 To: 'cervix uteri' From: 21 To: vagina From: 22 To: breast ----- We have redefined the number of classes to account for the real number of observations.

18 features

class (target)nominal21 unique values
0 missing
agenominal3 unique values
0 missing
sexnominal2 unique values
1 missing
histologic-typenominal3 unique values
67 missing
degree-of-diffenominal3 unique values
155 missing
bonenominal2 unique values
0 missing
bone-marrownominal2 unique values
0 missing
lungnominal2 unique values
0 missing
pleuranominal2 unique values
0 missing
peritoneumnominal2 unique values
0 missing
livernominal2 unique values
0 missing
brainnominal2 unique values
0 missing
skinnominal2 unique values
1 missing
necknominal2 unique values
0 missing
supraclavicularnominal2 unique values
0 missing
axillarnominal2 unique values
1 missing
mediastinumnominal2 unique values
0 missing
abdominalnominal2 unique values
0 missing

19 properties

339
Number of instances (rows) of the dataset.
18
Number of attributes (columns) of the dataset.
21
Number of distinct values of the target attribute (if it is nominal).
225
Number of missing values in the dataset.
207
Number of instances with at least one value missing.
0
Number of numeric attributes.
18
Number of nominal attributes.
84
Number of instances belonging to the most frequent class.
0.29
Percentage of instances belonging to the least frequent class.
1
Number of instances belonging to the least frequent class.
14
Number of binary attributes.
77.78
Percentage of binary attributes.
61.06
Percentage of instances having missing values.
0.1
Average class difference between consecutive instances.
3.69
Percentage of missing values.
0.05
Number of attributes divided by the number of instances.
0
Percentage of numeric attributes.
24.78
Percentage of instances belonging to the most frequent class.
100
Percentage of nominal attributes.

1 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
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