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胸外科数据集

胸外科数据集

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Life Classification

Creators: Marek Lubicz (1), Konrad Pawelczyk (2), Adam Rzechonek (2), Jerzy Kolodziej (2) -- (1) Wroclaw University of T......

数据结构 ? 23.7K

    Data Structure ?

    * 以上分析是由系统提取分析形成的结果,具体实际数据为准。

    README.md

    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), (+48) 71 320 44 53  

    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]}
    }

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