Data
thoracic-surgery

thoracic-surgery

active ARFF Publicly available Visibility: public Uploaded 25-05-2015 by Rafael G. Mantovani
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Author: Source: UCI Please cite: Zikeba, M., Tomczak, J. M., Lubicz, M., & Swikatek, 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. * Title: Thoracic Surgery Data Data Set * Abstract: The data is dedicated to classification problem related to the post-operative life expectancy in the lung cancer patients: class 1 - death within one year after surgery, class 2 - survival. * Source: 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 * 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 2007–2011. 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.

17 features

Class (target)nominal2 unique values
0 missing
V1nominal7 unique values
0 missing
V2numeric134 unique values
0 missing
V3numeric136 unique values
0 missing
V4nominal3 unique values
0 missing
V5nominal2 unique values
0 missing
V6nominal2 unique values
0 missing
V7nominal2 unique values
0 missing
V8nominal2 unique values
0 missing
V9nominal2 unique values
0 missing
V10nominal4 unique values
0 missing
V11nominal2 unique values
0 missing
V12nominal2 unique values
0 missing
V13nominal2 unique values
0 missing
V14nominal2 unique values
0 missing
V15nominal2 unique values
0 missing
V16numeric45 unique values
0 missing

107 properties

470
Number of instances (rows) of the dataset.
17
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
3
Number of numeric attributes.
14
Number of nominal attributes.
0.6
Second quartile (Median) of entropy among attributes.
0.15
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.21
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
85.11
Percentage of instances belonging to the most frequent class.
7.12
Mean standard deviation of attributes of the numeric type.
0.04
Minimal entropy among attributes.
0.75
Second quartile (Median) of kurtosis among attributes of the numeric type.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.61
Entropy of the target attribute values.
0.09
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
400
Number of instances belonging to the most frequent class.
-0.12
Minimum kurtosis among attributes of the numeric type.
4.57
Second quartile (Median) of means among attributes of the numeric type.
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.52
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
1.37
Maximum entropy among attributes.
3.28
Minimum of means among attributes of the numeric type.
0.01
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.15
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.15
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
30.48
Maximum kurtosis among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
0.55
Second quartile (Median) of skewness among attributes of the numeric type.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.01
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
62.53
Maximum of means among attributes of the numeric type.
2
The minimal number of distinct values among attributes of the nominal type.
64.71
Percentage of binary attributes.
8.71
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.53
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.04
Number of attributes divided by the number of instances.
0.02
Maximum mutual information between the nominal attributes and the target attribute.
-0.19
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
1.02
Third quartile of entropy among attributes.
0.27
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
87.18
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
7
The maximum number of distinct values among attributes of the nominal type.
0.87
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
30.48
Third quartile of kurtosis among attributes of the numeric type.
0.74
Average class difference between consecutive instances.
0.05
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
5.63
Maximum skewness among attributes of the numeric type.
14.89
Percentage of instances belonging to the least frequent class.
17.65
Percentage of numeric attributes.
62.53
Third quartile of means among attributes of the numeric type.
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.53
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.15
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
11.77
Maximum standard deviation of attributes of the numeric type.
70
Number of instances belonging to the least frequent class.
82.35
Percentage of nominal attributes.
0.01
Third quartile of mutual information between the nominal attributes and the target attribute.
0.15
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.27
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.61
Average entropy of the attributes.
0.62
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.24
First quartile of entropy among attributes.
5.63
Third quartile of skewness among attributes of the numeric type.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.05
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
10.37
Mean kurtosis among attributes of the numeric type.
0.3
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.12
First quartile of kurtosis among attributes of the numeric type.
11.77
Third quartile of standard deviation of attributes of the numeric type.
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.53
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.15
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
23.46
Mean of means among attributes of the numeric type.
0.12
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
3.28
First quartile of means among attributes of the numeric type.
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.15
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.27
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.01
Average mutual information between the nominal attributes and the target attribute.
11
Number of binary attributes.
0
First quartile of mutual information between the nominal attributes and the target attribute.
0.15
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.05
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
86.09
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
-0.19
First quartile of skewness among attributes of the numeric type.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
1.4
Standard deviation of the number of distinct values among attributes of the nominal type.
0.15
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
2.57
Average number of distinct values among the attributes of the nominal type.
0.87
First quartile of standard deviation of attributes of the numeric type.
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.15
Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.54
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
2
Mean skewness among attributes of the numeric type.

7 tasks

113 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
32 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - target_feature: Class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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