Data
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
V20numeric9 unique values
0 missing
V19numeric18 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
V10numeric57 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
V1numeric48 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

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

11 tasks

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