Data
ICU

ICU

active ARFF Publicly available Visibility: public Uploaded 07-10-2014 by Joaquin Vanschoren
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Author: Source: Unknown - Date unknown Please cite: Datasets of Data And Story Library, project illustrating use of basic statistic methods, converted to arff format by Hakan Kjellerstrand. Source: TunedIT: http://tunedit.org/repo/DASL DASL file http://lib.stat.cmu.edu/DASL/Datafiles/ICU.html Reference: Authorization: Contact authors Description: The data consist of 200 subjects from a larger study on the survival of patients following admission to an adult intensive care unit (ICU). The study used logistic regression to predict the probability of survival for these patients until their discharge from the hospital. The dependent variable is the binary variable Vital Status (STA). Nineteen possible predictor variables, both discrete and continuous, were also observed. Number of cases: 200 Variable Names: ID: ID number of the patient STA: Vital status (0 = Lived, 1 = Died) AGE: Patient's age in years SEX: Patient's sex (0 = Male, 1 = Female) RACE: Patient's race (1 = White, 2 = Black, 3 = Other) SER: Service at ICU admission (0 = Medical, 1 = Surgical) CAN: Is cancer part of the present problem? (0 = No, 1 = Yes) CRN: History of chronic renal failure (0 = No, 1 = Yes) INF:

20 features

LOC (target)numeric3 unique values
0 missing
ID (ignore)numeric200 unique values
0 missing
STAnumeric2 unique values
0 missing
AGEnumeric64 unique values
0 missing
SEXnumeric2 unique values
0 missing
RACnumeric3 unique values
0 missing
SERnumeric2 unique values
0 missing
CANnumeric2 unique values
0 missing
CRNnumeric2 unique values
0 missing
INFnumeric2 unique values
0 missing
CPRnumeric2 unique values
0 missing
SYSnumeric63 unique values
0 missing
HRAnumeric81 unique values
0 missing
PREnumeric2 unique values
0 missing
TYPnumeric2 unique values
0 missing
FRAnumeric2 unique values
0 missing
PO2numeric2 unique values
0 missing
PHnumeric2 unique values
0 missing
PCOnumeric2 unique values
0 missing
BICnumeric2 unique values
0 missing
CREnumeric2 unique values
0 missing

19 properties

200
Number of instances (rows) of the dataset.
20
Number of attributes (columns) of the dataset.
0
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.
20
Number of numeric attributes.
0
Number of nominal attributes.
0.78
Average class difference between consecutive instances.
0
Percentage of missing values.
0.1
Number of attributes divided by the number of instances.
100
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
0
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.

13 tasks

0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: LOC
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: LOC
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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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