Data
thoracic_surgery

thoracic_surgery

active ARFF Publicly available Visibility: public Uploaded 18-01-2016 by Hilda Fabiola Bernard
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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 Dataset from UCI Machine Learning Repository. 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.

17 features

Risk1Yr (target)nominal2 unique values
0 missing
DGNnominal7 unique values
0 missing
PRE4numeric134 unique values
0 missing
PRE5numeric136 unique values
0 missing
PRE6nominal3 unique values
0 missing
PRE7nominal2 unique values
0 missing
PRE8nominal2 unique values
0 missing
PRE9nominal2 unique values
0 missing
PRE10nominal2 unique values
0 missing
PRE11nominal2 unique values
0 missing
PRE14nominal4 unique values
0 missing
PRE17nominal2 unique values
0 missing
PRE19nominal2 unique values
0 missing
PRE25nominal2 unique values
0 missing
PRE30nominal2 unique values
0 missing
PRE32nominal2 unique values
0 missing
AGEnumeric45 unique values
0 missing

62 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.
62.53
Third quartile of means among attributes of the numeric type.
0.02
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
17.65
Percentage of numeric attributes.
0.01
Third quartile of mutual information between the nominal attributes and the target attribute.
7
The maximum number of distinct values among attributes of the nominal type.
-0.19
Minimum skewness among attributes of the numeric type.
82.35
Percentage of nominal attributes.
5.63
Third quartile of skewness among attributes of the numeric type.
5.63
Maximum skewness among attributes of the numeric type.
0.87
Minimum standard deviation of attributes of the numeric type.
0.24
First quartile of entropy among attributes.
11.77
Third quartile of standard deviation of attributes of the numeric type.
11.77
Maximum standard deviation of attributes of the numeric type.
14.89
Percentage of instances belonging to the least frequent class.
-0.12
First quartile of kurtosis among attributes of the numeric type.
1.4
Standard deviation of the number of distinct values among attributes of the nominal type.
0.61
Average entropy of the attributes.
70
Number of instances belonging to the least frequent class.
3.28
First quartile of means among attributes of the numeric type.
10.37
Mean kurtosis among attributes of the numeric type.
11
Number of binary attributes.
0
First quartile of mutual information between the nominal attributes and the target attribute.
23.46
Mean of means among attributes of the numeric type.
-0.19
First quartile of skewness among attributes of the numeric type.
0.74
Average class difference between consecutive instances.
0.01
Average mutual information between the nominal attributes and the target attribute.
0.87
First quartile of standard deviation of attributes of the numeric type.
0.61
Entropy of the target attribute values.
86.09
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
0.6
Second quartile (Median) of entropy among attributes.
0.04
Number of attributes divided by the number of instances.
2.57
Average number of distinct values among the attributes of the nominal type.
0.75
Second quartile (Median) of kurtosis among attributes of the numeric type.
87.18
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
2
Mean skewness among attributes of the numeric type.
4.57
Second quartile (Median) of means among attributes of the numeric type.
85.11
Percentage of instances belonging to the most frequent class.
7.12
Mean standard deviation of attributes of the numeric type.
0.01
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
400
Number of instances belonging to the most frequent class.
0.04
Minimal entropy among attributes.
0.55
Second quartile (Median) of skewness among attributes of the numeric type.
1.37
Maximum entropy among attributes.
-0.12
Minimum kurtosis among attributes of the numeric type.
64.71
Percentage of binary attributes.
8.71
Second quartile (Median) of standard deviation of attributes of the numeric type.
1.02
Third quartile of entropy among attributes.
30.48
Maximum kurtosis among attributes of the numeric type.
3.28
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
30.48
Third quartile of kurtosis among attributes of the numeric type.
62.53
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.

4 tasks

31 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Risk1Yr
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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