{ "data_id": "171", "name": "primary-tumor", "exact_name": "primary-tumor", "version": 1, "version_label": "1", "description": "**Author**: \n**Source**: Unknown - \n**Please cite**: \n\nCitation Request:\n This primary tumor domain was obtained from the University Medical Centre,\n Institute of Oncology, Ljubljana, Yugoslavia. Thanks go to M. Zwitter and \n M. Soklic for providing the data. Please include this citation if you plan\n to use this database.\n \n 1. Title: Primary Tumor Domain\n \n 2. Sources:\n (a) Source:\n (b) Donors: Igor Kononenko, \n University E.Kardelj\n Faculty for electrical engineering\n Trzaska 25\n 61000 Ljubljana (tel.: (38)(+61) 265-161\n \n Bojan Cestnik\n Jozef Stefan Institute\n Jamova 39\n 61000 Ljubljana\n Yugoslavia (tel.: (38)(+61) 214-399 ext.287) \n (c) Date: November 1988\n \n 3. Past Usage: (sveral)\n 1. Cestnik,G., Konenenko,I, & Bratko,I. (1987). Assistant-86: A\n Knowledge-Elicitation Tool for Sophisticated Users. In I.Bratko\n & N.Lavrac (Eds.) Progress in Machine Learning, 31-45, Sigma Press.\n -- Assistant-86: 44% accuracy\n 2. Clark,P. & Niblett,T. (1987). Induction in Noisy Domains. In\n I.Bratko & N.Lavrac (Eds.) Progress in Machine Learning, 11-30,\n Sigma Press.\n -- Simple Bayes: 48% accuracy\n -- CN2 (95% threshold): 45%\n 3. Michalski,R., Mozetic,I. Hong,J., & Lavrac,N. (1986). The Multi-Purpose\n Incremental Learning System AQ15 and its Testing Applications to Three\n Medical Domains. In Proceedings of the Fifth National Conference on\n Artificial Intelligence, 1041-1045. Philadelphia, PA: Morgan Kaufmann.\n -- Experts: 42% accuracy \n -- AQ15: 29-41%\n \n 4. Relevant Information:\n This is one of three domains provided by the Oncology Institute\n that has repeatedly appeared in the machine learning literature.\n (See also breast-cancer and lymphography.)\n \n 5. Number of Instances: 339\n \n 6. Number of Attributes: 18 including the class attribute\n \n 7. Attribute Information: (class is location of tumor)\n --- NOTE: All attribute values in the database have been entered as\n numeric values corresponding to their index in the list\n of attribute values for that attribute domain as given below.\n 1. class: lung, head & neck, esophasus, thyroid, stomach, duoden & sm.int,\n colon, rectum, anus, salivary glands, pancreas, gallblader,\n liver, kidney, bladder, testis, prostate, ovary, corpus uteri, \n cervix uteri, vagina, breast\n 2. age: <30, 30-59, >=60\n 3. sex: male, female\n 4. histologic-type: epidermoid, adeno, anaplastic\n 5. degree-of-diffe: well, fairly, poorly\n 6. bone: yes, no\n 7. bone-marrow: yes, no\n 8. lung: yes, no\n 9. pleura: yes, no\n 10. peritoneum: yes, no\n 11. liver: yes, no\n 12. brain: yes, no\n 13. skin: yes, no\n 14. neck: yes, no\n 15. supraclavicular: yes, no\n 16. axillar: yes, no\n 17. mediastinum: yes, no\n 18. abdominal: yes, no\n \n 8. Missing Attribute Values: (? indicates unknown value)\n Attribute#: Number of missing values\n 1: 0\n 2: 0\n 3: 1\n 4: 67\n 5: 155\n 6: 0\n 7: 0\n 8: 0\n 9: 0\n 10: 0\n 11: 0\n 12: 0\n 13: 1\n 14: 0\n 15: 0\n 16: 1\n 17: 0\n 18: 0\n \n 9. Class Distribution: \n Class Index: Number of instances in class:\n 1: 84\n 2: 20\n 3: 9\n 4: 14\n 5: 39\n 6: 1\n 7: 14\n 8: 6\n 9: 0\n 10: 2\n 11: 28\n 12: 16\n 13: 7\n 14: 24\n 15: 2\n 16: 1\n 17: 10\n 18: 29\n 19: 6\n 20: 2\n 21: 1\n 22: 24\n\n\n\n\n\n Relabeled values in attribute age\n From: 1 To: '<30' \n From: 2 To: '30-59' \n From: 3 To: '>=60' \n\n\n Relabeled values in attribute sex\n From: 1 To: male \n From: 2 To: female \n\n\n Relabeled values in attribute histologic-type\n From: 1 To: epidermoid \n From: 2 To: adeno \n From: 3 To: anaplastic \n\n\n Relabeled values in attribute degree-of-diffe\n From: 1 To: well \n From: 2 To: fairly \n From: 3 To: poorly \n\n\n Relabeled values in attribute bone\n From: 1 To: yes \n From: 2 To: no \n\n\n Relabeled values in attribute bone-marrow\n From: 1 To: yes \n From: 2 To: no \n\n\n Relabeled values in attribute lung\n From: 1 To: yes \n From: 2 To: no \n\n\n Relabeled values in attribute pleura\n From: 1 To: yes \n From: 2 To: no \n\n\n Relabeled values in attribute peritoneum\n From: 1 To: yes \n From: 2 To: no \n\n\n Relabeled values in attribute liver\n From: 1 To: yes \n From: 2 To: no \n\n\n Relabeled values in attribute brain\n From: 1 To: yes \n From: 2 To: no \n\n\n Relabeled values in attribute skin\n From: 1 To: yes \n From: 2 To: no \n\n\n Relabeled values in attribute neck\n From: 1 To: yes \n From: 2 To: no \n\n\n Relabeled values in attribute supraclavicular\n From: 1 To: yes \n From: 2 To: no \n\n\n Relabeled values in attribute axillar\n From: 1 To: yes \n From: 2 To: no \n\n\n Relabeled values in attribute mediastinum\n From: 1 To: yes \n From: 2 To: no \n\n\n Relabeled values in attribute abdominal\n From: 1 To: yes \n From: 2 To: no \n\n\n Relabeled values in attribute class\n From: 1 To: lung \n From: 2 To: 'head and neck' \n From: 3 To: esophagus \n From: 4 To: thyroid \n From: 5 To: stomach \n From: 6 To: 'duoden and sm.int' \n From: 7 To: colon \n From: 8 To: rectum \n From: 9 To: anus \n From: 10 To: 'salivary glands' \n From: 11 To: pancreas \n From: 12 To: gallbladder \n From: 13 To: liver \n From: 14 To: kidney \n From: 15 To: bladder \n From: 16 To: testis \n From: 17 To: prostate \n From: 18 To: ovary \n From: 19 To: 'corpus uteri' \n From: 20 To: 'cervix uteri' \n From: 21 To: vagina \n From: 22 To: breast", "format": "ARFF", "uploader": "Jan van Rijn", "uploader_id": 1, "visibility": "public", "creator": "University Medical Centre, Institute of Oncology, Ljubljana, Slovenia", "contributor": null, "date": "2014-04-23 13:11:56", "update_comment": null, "last_update": "2014-04-23 13:11:56", "licence": "Public", "status": "active", "error_message": null, "url": "https:\/\/www.openml.org\/data\/download\/3594\/dataset_113_primary-tumor.arff", "default_target_attribute": "class", "row_id_attribute": null, "ignore_attribute": null, "runs": 1261, "suggest": { "input": [ "primary-tumor", "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. 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