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cardiotocography

cardiotocography

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Author: J. P. Marques de Sá, J. Bernardes, D. Ayers de Campos. Source: UCI Please cite: * Source: Marques de Sá, J.P., jpmdesa '@' gmail.com, Biomedical Engineering Institute, Porto, Portugal. Bernardes, J., joaobern '@' med.up.pt, Faculty of Medicine, University of Porto, Portugal. Ayres de Campos, D., sisporto '@' med.up.pt, Faculty of Medicine, University of Porto, Portugal. * Data Set Information: 2126 fetal cardiotocograms (CTGs) were automatically processed and the respective diagnostic features measured. The CTGs were also classified by three expert obstetricians and a consensus classification label assigned to each of them. Classification was both with respect to a morphologic pattern (A, B, C. ...) and to a fetal state (N, S, P). Therefore the dataset can be used either for 10-class or 3-class experiments. * Attribute Information: LB - FHR baseline (beats per minute) AC - # of accelerations per second FM - # of fetal movements per second UC - # of uterine contractions per second DL - # of light decelerations per second DS - # of severe decelerations per second DP - # of prolongued decelerations per second ASTV - percentage of time with abnormal short term variability MSTV - mean value of short term variability ALTV - percentage of time with abnormal long term variability MLTV - mean value of long term variability Width - width of FHR histogram Min - minimum of FHR histogram Max - Maximum of FHR histogram Nmax - # of histogram peaks Nzeros - # of histogram zeros Mode - histogram mode Mean - histogram mean Median - histogram median Variance - histogram variance Tendency - histogram tendency CLASS - FHR pattern class code (1 to 10) NSP - fetal state class code (N=normal; S=suspect; P=pathologic) * Relevant Papers: Ayres de Campos et al. (2000) SisPorto 2.0 A Program for Automated Analysis of Cardiotocograms. J Matern Fetal Med 5:311-318

36 features

Class (target)nominal10 unique values
0 missing
V1numeric48 unique values
0 missing
V2numeric979 unique values
0 missing
V3numeric1064 unique values
0 missing
V4numeric48 unique values
0 missing
V5numeric48 unique values
0 missing
V6numeric22 unique values
0 missing
V7numeric96 unique values
0 missing
V8numeric19 unique values
0 missing
V9numeric75 unique values
0 missing
V10numeric57 unique values
0 missing
V11numeric87 unique values
0 missing
V12numeric249 unique values
0 missing
V13numeric15 unique values
0 missing
V14numeric2 unique values
0 missing
V15numeric5 unique values
0 missing
V16numeric154 unique values
0 missing
V17numeric109 unique values
0 missing
V18numeric86 unique values
0 missing
V19numeric18 unique values
0 missing
V20numeric9 unique values
0 missing
V21numeric88 unique values
0 missing
V22numeric103 unique values
0 missing
V23numeric95 unique values
0 missing
V24numeric133 unique values
0 missing
V25numeric3 unique values
0 missing
V26numeric2 unique values
0 missing
V27numeric2 unique values
0 missing
V28numeric2 unique values
0 missing
V29numeric2 unique values
0 missing
V30numeric2 unique values
0 missing
V31numeric2 unique values
0 missing
V32numeric2 unique values
0 missing
V33numeric2 unique values
0 missing
V34numeric2 unique values
0 missing
V35numeric2 unique values
0 missing

107 properties

2126
Number of instances (rows) of the dataset.
36
Number of attributes (columns) of the dataset.
10
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.
35
Number of numeric attributes.
1
Number of nominal attributes.
Average mutual information between the nominal attributes and the target attribute.
0.96
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.13
First quartile of means among attributes of the numeric type.
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0
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
0.05
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0.01
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
1
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
0.95
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
10
Average number of distinct values among the attributes of the nominal type.
0.12
First quartile of skewness among attributes of the numeric type.
0.99
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
1
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
Standard deviation of the number of distinct values among attributes of the nominal type.
0
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
2.39
Mean skewness among attributes of the numeric type.
0.36
First quartile of standard deviation of attributes of the numeric type.
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0
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
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
60.5
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.01
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1
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
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
27.23
Percentage of instances belonging to the most frequent class.
Minimal entropy among attributes.
3.01
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.99
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
2.91
Entropy of the target attribute values.
1
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
579
Number of instances belonging to the most frequent class.
-1.37
Minimum kurtosis among attributes of the numeric type.
3.66
Second quartile (Median) of means among attributes of the numeric type.
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
0
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.01
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.55
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
299.42
Maximum kurtosis among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
1.66
Second quartile (Median) of skewness among attributes of the numeric type.
0.99
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.31
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
1702.88
Maximum of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
10
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of binary attributes.
2.95
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.02
Number of attributes divided by the number of instances.
10
The maximum number of distinct values among attributes of the nominal type.
-1
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.05
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
17.35
Maximum skewness among attributes of the numeric type.
0.06
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
15.13
Third quartile of kurtosis among attributes of the numeric type.
0.61
Average class difference between consecutive instances.
0.95
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
930.92
Maximum standard deviation of attributes of the numeric type.
2.49
Percentage of instances belonging to the least frequent class.
97.22
Percentage of numeric attributes.
93.58
Third quartile of means among attributes of the numeric type.
1
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
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
53
Number of instances belonging to the least frequent class.
2.78
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0
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
0.05
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
17.74
Mean kurtosis among attributes of the numeric type.
0.99
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
3.92
Third quartile of skewness among attributes of the numeric type.
1
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
0.95
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
106.21
Mean of means among attributes of the numeric type.
0.04
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.29
First quartile of kurtosis among attributes of the numeric type.
17.19
Third quartile of standard deviation of attributes of the numeric type.
1
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
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001

11 tasks

10 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 10% Holdout set - target_feature: Class
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: Test on Training Data - target_feature: Class
0 runs - estimation_procedure: Leave one out - target_feature: Class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - target_feature: Class
0 runs - estimation_procedure: 10 times 10-fold Learning Curve - target_feature: Class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: Class
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: Class
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