胸外科数据集

Creators: Marek Lubicz (1), Konrad Pawelczyk (2), Adam Rzechonek (2), Jerzy Kolodziej (2) -- (1) Wroclaw University of Technology, wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland -- (2) Wroclaw Medical University, wybrzeze L. Pasteura 1, 50-367 Wroclaw, Poland Donor: Maciej Zieba (maciej.zieba '@' pwr.wroc.pl), Jakub M. Tomczak (jakub.tomczak '@' pwr.wroc.pl), ([phone] Date: November, 2013 Data Set Information: The data was collected retrospectively at Wroclaw Thoracic Surgery Centre for patients who underwent major lung resections for primary lung cancer in the years 2007a€“2011. The Centre is associated with the Department of Thoracic Surgery of the Medical University of Wroclaw and Lower-Silesian Centre for Pulmonary Diseases, Poland, while the research database constitutes a part of the National Lung Cancer Registry, administered by the Institute of Tuberculosis and Pulmonary Diseases in Warsaw, Poland. Attribute Information: 1. DGN: Diagnosis - specific combination of ICD-10 codes for primary and secondary as well multiple tumours if any (DGN3,DGN2,DGN4,DGN6,DGN5,DGN8,DGN1) 2. PRE4: Forced vital capacity - FVC (numeric) 3. PRE5: Volume that has been exhaled at the end of the first second of forced expiration - FEV1 (numeric) 4. PRE6: Performance status - Zubrod scale (PRZ2,PRZ1,PRZ0) 5. PRE7: Pain before surgery (T,F) 6. PRE8: Haemoptysis before surgery (T,F) 7. PRE9: Dyspnoea before surgery (T,F) 8. PRE10: Cough before surgery (T,F) 9. PRE11: Weakness before surgery (T,F) 10. PRE14: T in clinical TNM - size of the original tumour, from OC11 (smallest) to OC14 (largest) (OC11,OC14,OC12,OC13) 11. PRE17: Type 2 DM - diabetes mellitus (T,F) 12. PRE19: MI up to 6 months (T,F) 13. PRE25: PAD - peripheral arterial diseases (T,F) 14. PRE30: Smoking (T,F) 15. PRE32: Asthma (T,F) 16. AGE: Age at surgery (numeric) 17. Risk1Y: 1 year survival period - (T)rue value if died (T,F) Class Distribution: the class value (Risk1Y) is binary valued. Risk1Y Value: Number of Instances: T 70 N 400 Summary Statistics: Binary Attributes Distribution: PRE7 Value: Number of Instances: T 31 N 439 PRE8 Value: Number of Instances: T 68 N 402 PRE9 Value: Number of Instances: T 31 N 439 PRE10 Value: Number of Instances: T 323 N 147 PRE11 Value: Number of Instances: T 78 N 392 PRE17 Value: Number of Instances: T 35 N 435 PRE19 Value: Number of Instances: T 2 N 468 PRE25 Value: Number of Instances: T 8 N 462 PRE30 Value: Number of Instances: T 386 N 84 PRE32 Value: Number of Instances: T 368 N 2 Nominal Attributes Distribution: DGN Value: Number of Instances: DGN3 349 DGN2 52 DGN4 47 DGN6 4 DGN5 15 DGN8 2 DGN1 1 PRE6 Value: Number of Instances: PRZ2 27 PRZ1 313 PRZ0 130 PRE14 Value: Number of Instances: OC11 177 OC14 17 OC12 257 OC13 19 Numeric Attributes Statistics: Min Max Mean SD PRE4: 1.4 6.3 3.3 0.9 PRE5: 0.96 86.3 4.6 11.8 AGE: 21 87 52.5 8.7 Relevant Papers: Zi??ba, M., Tomczak, J. M., Lubicz, M., & ??wi?…tek, J. (2013). Boosted SVM for extracting rules from imbalanced data in application to prediction of the post-operative life expectancy in the lung cancer patients. Applied Soft Computing. [Web link] - Results: -- Boosted SVM for for imbalanced data gained the Gmean value equal 0.657, -- Decision rules induced using Boosted SVM as an oracle gained the Gmean value equal 0.648. Citation Request: Zi??ba, M., Tomczak, J. M., Lubicz, M., & ??wi?…tek, J. (2013). Boosted SVM for extracting rules from imbalanced data in application to prediction of the post-operative life expectancy in the lung cancer patients. Applied Soft Computing. [Web link] BibTeX: @article{zieba2013boosted, title={Boosted SVM for extracting rules from imbalanced data in application to prediction of the post-operative life expectancy in the lung cancer patients}, author={Zi{k{e}}ba, Maciej and Tomczak, Jakub M and Lubicz, Marek and {'S}wi{k{a}}tek, Jerzy}, journal={Applied Soft Computing}, year={2013}, publisher={Elsevier}, doi={[Web link]} }