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postoperative-patient-data

postoperative-patient-data

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Author: Source: Unknown - Please cite: 1. Title: Postoperative Patient Data 2. Source Information: -- Creators: Sharon Summers, School of Nursing, University of Kansas Medical Center, Kansas City, KS 66160 Linda Woolery, School of Nursing, University of Missouri, Columbia, MO 65211 -- Donor: Jerzy W. Grzymala-Busse (jerzy@cs.ukans.edu) (913)864-4488 -- Date: June 1993 3. Past Usage: 1. A. Budihardjo, J. Grzymala-Busse, L. Woolery (1991). Program LERS_LB 2.5 as a tool for knowledge acquisition in nursing, Proceedings of the 4th Int. Conference on Industrial & Engineering Applications of AI & Expert Systems, pp. 735-740. 2. L. Woolery, J. Grzymala-Busse, S. Summers, A. Budihardjo (1991). The use of machine learning program LERS_LB 2.5 in knowledge acquisition for expert system development in nursing. Computers in Nursing 9, pp. 227-234. 4. Relevant Information: The classification task of this database is to determine where patients in a postoperative recovery area should be sent to next. Because hypothermia is a significant concern after surgery (Woolery, L. et. al. 1991), the attributes correspond roughly to body temperature measurements. Results: -- LERS (LEM2): 48% accuracy 5. Number of Instances: 90 6. Number of Attributes: 9 including the decision (class attribute) 7. Attribute Information: 1. L-CORE (patient's internal temperature in C): high (> 37), mid (>= 36 and <= 37), low (< 36) 2. L-SURF (patient's surface temperature in C): high (> 36.5), mid (>= 36.5 and <= 35), low (< 35) 3. L-O2 (oxygen saturation in %): excellent (>= 98), good (>= 90 and < 98), fair (>= 80 and < 90), poor (< 80) 4. L-BP (last measurement of blood pressure): high (> 130/90), mid (<= 130/90 and >= 90/70), low (< 90/70) 5. SURF-STBL (stability of patient's surface temperature): stable, mod-stable, unstable 6. CORE-STBL (stability of patient's core temperature) stable, mod-stable, unstable 7. BP-STBL (stability of patient's blood pressure) stable, mod-stable, unstable 8. COMFORT (patient's perceived comfort at discharge, measured as an integer between 0 and 20) 9. decision ADM-DECS (discharge decision): I (patient sent to Intensive Care Unit), S (patient prepared to go home), A (patient sent to general hospital floor) 8. Missing Attribute Values: Attribute 8 has 3 missing values 9. Class Distribution: I (2) S (24) A (64) Information about the dataset CLASSTYPE: nominal CLASSINDEX: last

9 features

decision (target)nominal3 unique values
0 missing
L-COREnominal3 unique values
0 missing
L-SURFnominal3 unique values
0 missing
L-O2nominal2 unique values
0 missing
L-BPnominal3 unique values
0 missing
SURF-STBLnominal2 unique values
0 missing
CORE-STBLnominal3 unique values
0 missing
BP-STBLnominal3 unique values
0 missing
COMFORTnominal4 unique values
3 missing

108 properties

90
Number of instances (rows) of the dataset.
9
Number of attributes (columns) of the dataset.
3
Number of distinct values of the target attribute (if it is nominal).
3
Number of missing values in the dataset.
3
Number of instances with at least one value missing.
0
Number of numeric attributes.
9
Number of nominal attributes.
0
Maximum kurtosis among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
0.02
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.29
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
-0.02
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0
Maximum of means among attributes of the numeric type.
2
The minimal number of distinct values among attributes of the nominal type.
0
Second quartile (Median) of skewness among attributes of the numeric type.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.71
The predictive accuracy obtained by always predicting the majority class.
0.05
Maximum mutual information between the nominal attributes and the target attribute.
0
Minimum skewness among attributes of the numeric type.
22.22
Percentage of binary attributes.
0
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.46
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.1
Number of attributes divided by the number of instances.
4
The maximum number of distinct values among attributes of the nominal type.
0
Minimum standard deviation of attributes of the numeric type.
3.33
Percentage of instances having missing values.
1.41
Third quartile of entropy among attributes.
0.63
Average class difference between consecutive instances.
0.46
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
43.45
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0
Maximum skewness among attributes of the numeric type.
0.02
Percentage of instances belonging to the least frequent class.
0.37
Percentage of missing values.
0
Third quartile of kurtosis 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
-0.1
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
0
Maximum standard deviation of attributes of the numeric type.
2
Number of instances belonging to the least frequent class.
0
Percentage of numeric attributes.
0
Third quartile of means among attributes of the numeric type.
0.29
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
0.46
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.29
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.11
Average entropy of the attributes.
0.4
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
100
Percentage of nominal attributes.
0.03
Third quartile of mutual information between the nominal attributes and the target attribute.
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
0.46
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
Mean kurtosis among attributes of the numeric type.
0.37
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1
First quartile of entropy among attributes.
0
Third quartile of skewness among 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
-0.1
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
0
Mean of means among attributes of the numeric type.
-0.13
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0
First quartile of kurtosis among attributes of the numeric type.
0
Third quartile of standard deviation of attributes of the numeric type.
0.29
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
0.46
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.29
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.02
Average mutual information between the nominal attributes and the target attribute.
48.25
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
0
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
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
0.46
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
2.88
Average number of distinct values among the attributes of the nominal type.
2
Number of binary attributes.
0.01
First quartile of mutual information between the nominal attributes and the target attribute.
0.29
Error rate 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
-0.1
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
0
Mean skewness among attributes of the numeric type.
0
First quartile of skewness among attributes of the numeric type.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.29
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
0.64
Standard deviation of the number of distinct values among attributes of the nominal type.
0.29
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
0
Mean standard deviation of attributes of the numeric type.
0
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
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
0.41
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
71.11
Percentage of instances belonging to the most frequent class.
0.44
Minimal entropy among attributes.
1.11
Second quartile (Median) of entropy among attributes.
0.29
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.98
Entropy of the target attribute values.
0.36
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
64
Number of instances belonging to the most frequent class.
0
Minimum kurtosis among attributes of the numeric type.
0
Second quartile (Median) of kurtosis among attributes of the numeric type.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.47
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
-0.07
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
1.49
Maximum entropy among attributes.
0
Minimum of means among attributes of the numeric type.
0
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
0.3
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump

9 tasks

677 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: decision
309 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: decision
300 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: decision
176 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: decision
0 runs - estimation_procedure: Leave one out - evaluation_measure: predictive_accuracy - target_feature: decision
168 runs - estimation_procedure: 10 times 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: decision
77 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: decision
26 runs - estimation_procedure: Interleaved Test then Train - target_feature: decision
0 runs - estimation_procedure: 50 times Clustering
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