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heart-statlog

heart-statlog

active ARFF Publicly available Visibility: public Uploaded 06-04-2014 by Jan van Rijn
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Author: Source: Unknown - Please cite: This database contains 13 attributes (which have been extracted from a larger set of 75) Attribute Information: ------------------------ -- 1. age -- 2. sex -- 3. chest pain type (4 values) -- 4. resting blood pressure -- 5. serum cholestoral in mg/dl -- 6. fasting blood sugar > 120 mg/dl -- 7. resting electrocardiographic results (values 0,1,2) -- 8. maximum heart rate achieved -- 9. exercise induced angina -- 10. oldpeak = ST depression induced by exercise relative to rest -- 11. the slope of the peak exercise ST segment -- 12. number of major vessels (0-3) colored by flourosopy -- 13. thal: 3 = normal; 6 = fixed defect; 7 = reversable defect Attributes types ----------------- Real: 1,4,5,8,10,12 Ordered:11, Binary: 2,6,9 Nominal:7,3,13 Variable to be predicted ------------------------ Absence (1) or presence (2) of heart disease Cost Matrix abse pres absence 0 1 presence 5 0 where the rows represent the true values and the columns the predicted. No missing values. 270 observations Relabeled values in attribute class From: 1 To: absent From: 2 To: present

14 features

class (target)nominal2 unique values
0 missing
agenumeric41 unique values
0 missing
sexnumeric2 unique values
0 missing
chestnumeric4 unique values
0 missing
resting_blood_pressurenumeric47 unique values
0 missing
serum_cholestoralnumeric144 unique values
0 missing
fasting_blood_sugarnumeric2 unique values
0 missing
resting_electrocardiographic_resultsnumeric3 unique values
0 missing
maximum_heart_rate_achievednumeric90 unique values
0 missing
exercise_induced_anginanumeric2 unique values
0 missing
oldpeaknumeric39 unique values
0 missing
slopenumeric3 unique values
0 missing
number_of_major_vesselsnumeric4 unique values
0 missing
thalnumeric3 unique values
0 missing

107 properties

270
Number of instances (rows) of the dataset.
14
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.
13
Number of numeric attributes.
1
Number of nominal attributes.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
1
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0.25
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.63
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
0.4
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
2
Average number of distinct values among the attributes of the nominal type.
-0.35
First quartile of skewness among attributes of the numeric type.
0.49
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.8
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.
0.2
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.43
Mean skewness among attributes of the numeric type.
0.54
First quartile of standard deviation of attributes of the numeric type.
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.19
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
0.73
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.6
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
8.44
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.25
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.63
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
0.27
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
55.56
Percentage of instances belonging to the most frequent class.
Minimal entropy among attributes.
-0.3
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.49
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.99
Entropy of the target attribute values.
0.46
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
150
Number of instances belonging to the most frequent class.
-2.01
Minimum kurtosis among attributes of the numeric type.
1.59
Second quartile (Median) of means among attributes of the numeric type.
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
0.15
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.25
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.27
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
4.9
Maximum kurtosis among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0.54
Second quartile (Median) of skewness among attributes of the numeric type.
0.49
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.46
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
249.66
Maximum of means among attributes of the numeric type.
2
The minimal number of distinct values among attributes of the nominal type.
7.14
Percentage of binary attributes.
1
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.7
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.05
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
-0.88
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.3
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.
2
The maximum number of distinct values among attributes of the nominal type.
0.36
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
1.34
Third quartile of kurtosis among attributes of the numeric type.
0.49
Average class difference between consecutive instances.
0.4
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.99
Maximum skewness among attributes of the numeric type.
44.44
Percentage of instances belonging to the least frequent class.
92.86
Percentage of numeric attributes.
92.89
Third quartile of means among attributes of the numeric type.
0.8
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
0.7
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.2
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
51.69
Maximum standard deviation of attributes of the numeric type.
120
Number of instances belonging to the least frequent class.
7.14
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.19
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
0.3
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.6
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
1.2
Third quartile of skewness among attributes of the numeric type.
0.63
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
0.4
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.12
Mean kurtosis among attributes of the numeric type.
0.18
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-1.45
First quartile of kurtosis among attributes of the numeric type.
13.49
Third quartile of standard deviation of attributes of the numeric type.
0.8
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
0.7
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.2
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
46.04
Mean of means among attributes of the numeric type.
Average mutual information between the nominal attributes and the target attribute.
0.64
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.67
First quartile of means among attributes of the numeric type.
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.19
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
0.3
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.6
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001

15 tasks

1419 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
881 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
351 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
203 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
31 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
0 runs - estimation_procedure: Leave one out - evaluation_measure: predictive_accuracy - target_feature: class
213 runs - estimation_procedure: 10 times 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
85 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
25 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
0 runs - estimation_procedure: 50 times Clustering
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