Data
SPECT

SPECT

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Author: Krzysztof J. Cios","Lukasz A. Source: [original](https://archive.ics.uci.edu/ml/datasets/SPECT+Heart) - Please cite: SPECT heart data This is a merged version of the separate train and test set which are usually distributed. On OpenML this train-test split can be found as one of the possible tasks. Sources: -- Original owners: Krzysztof J. Cios, Lukasz A. Kurgan University of Colorado at Denver, Denver, CO 80217, U.S.A. Krys.Cios@cudenver.edu Lucy S. Goodenday Medical College of Ohio, OH, U.S.A. -- Donors: Lukasz A.Kurgan, Krzysztof J. Cios -- Date: 10/01/01 Relevant Information: The dataset describes diagnosing of cardiac Single Proton Emission Computed Tomography (SPECT) images. Each of the patients is classified into two categories: normal and abnormal. The database of 267 SPECT image sets (patients) was processed to extract features that summarize the original SPECT images. As a result, 44 continuous feature pattern was created for each patient. The pattern was further processed to obtain 22 binary feature patterns. The CLIP3 algorithm was used to generate classification rules from these patterns. The CLIP3 algorithm generated rules that were 84.0% accurate (as compared with cardiologists' diagnoses). Attribute Information: 1. OVERALL_DIAGNOSIS: 0,1 (class attribute, binary) 2. F1: 0,1 (the partial diagnosis 1, binary) 3. F2: 0,1 (the partial diagnosis 2, binary) 4. F3: 0,1 (the partial diagnosis 3, binary) 5. F4: 0,1 (the partial diagnosis 4, binary) 6. F5: 0,1 (the partial diagnosis 5, binary) 7. F6: 0,1 (the partial diagnosis 6, binary) 8. F7: 0,1 (the partial diagnosis 7, binary) 9. F8: 0,1 (the partial diagnosis 8, binary) 10. F9: 0,1 (the partial diagnosis 9, binary) 11. F10: 0,1 (the partial diagnosis 10, binary) 12. F11: 0,1 (the partial diagnosis 11, binary) 13. F12: 0,1 (the partial diagnosis 12, binary) 14. F13: 0,1 (the partial diagnosis 13, binary) 15. F14: 0,1 (the partial diagnosis 14, binary) 16. F15: 0,1 (the partial diagnosis 15, binary) 17. F16: 0,1 (the partial diagnosis 16, binary) 18. F17: 0,1 (the partial diagnosis 17, binary) 19. F18: 0,1 (the partial diagnosis 18, binary) 20. F19: 0,1 (the partial diagnosis 19, binary) 21. F20: 0,1 (the partial diagnosis 20, binary) 22. F21: 0,1 (the partial diagnosis 21, binary) 23. F22: 0,1 (the partial diagnosis 22, binary)

23 features

OVERALL_DIAGNOSIS (target)nominal2 unique values
0 missing
F1nominal2 unique values
0 missing
F2nominal2 unique values
0 missing
F3nominal2 unique values
0 missing
F4nominal2 unique values
0 missing
F5nominal2 unique values
0 missing
F6nominal2 unique values
0 missing
F7nominal2 unique values
0 missing
F8nominal2 unique values
0 missing
F9nominal2 unique values
0 missing
F10nominal2 unique values
0 missing
F11nominal2 unique values
0 missing
F12nominal2 unique values
0 missing
F13nominal2 unique values
0 missing
F14nominal2 unique values
0 missing
F15nominal2 unique values
0 missing
F16nominal2 unique values
0 missing
F17nominal2 unique values
0 missing
F18nominal2 unique values
0 missing
F19nominal2 unique values
0 missing
F20nominal2 unique values
0 missing
F21nominal2 unique values
0 missing
F22nominal2 unique values
0 missing

107 properties

267
Number of instances (rows) of the dataset.
23
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.
0
Number of numeric attributes.
23
Number of nominal attributes.
19.04
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
23
Number of binary attributes.
0.03
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
0.36
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
0.38
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.73
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.
First quartile of skewness among attributes of the numeric type.
0.17
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.7
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.
0.21
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
Mean skewness among attributes of the numeric type.
First quartile of standard deviation of attributes of the numeric type.
0.61
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.23
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
0.41
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
Mean standard deviation of attributes of the numeric type.
0.89
Second quartile (Median) of entropy among attributes.
0.25
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.36
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
0.25
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
79.4
Percentage of instances belonging to the most frequent class.
0.56
Minimal entropy among attributes.
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.17
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.73
Entropy of the target attribute values.
0.37
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
212
Number of instances belonging to the most frequent class.
Minimum kurtosis among attributes of the numeric type.
Second quartile (Median) of means among attributes of the numeric type.
0.61
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
1
Maximum entropy among attributes.
Minimum of means among attributes of the numeric type.
0.04
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
0.21
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum kurtosis among attributes of the numeric type.
0.02
Minimal mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of skewness among attributes of the numeric type.
0.17
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum of means among attributes of the numeric type.
2
The minimal number of distinct values among attributes of the nominal type.
100
Percentage of binary attributes.
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
0.09
Number of attributes divided by the number of instances.
0.11
Maximum mutual information between the nominal attributes and the target attribute.
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
0.97
Third quartile of entropy among attributes.
0.21
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
17.02
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.
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
Third quartile of kurtosis among attributes of the numeric type.
0.99
Average class difference between consecutive instances.
0.38
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.73
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Maximum skewness among attributes of the numeric type.
20.6
Percentage of instances belonging to the least frequent class.
0
Percentage of numeric attributes.
Third quartile of means among attributes of the numeric type.
0.7
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
0.21
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Maximum standard deviation of attributes of the numeric type.
55
Number of instances belonging to the least frequent class.
100
Percentage of nominal attributes.
0.05
Third quartile of mutual information between the nominal attributes and the target attribute.
0.23
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
0.21
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.41
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.86
Average entropy of the attributes.
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.81
First quartile of entropy among attributes.
Third quartile of skewness among attributes of the numeric type.
0.36
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
0.38
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.73
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Mean kurtosis among attributes of the numeric type.
0.22
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of kurtosis among attributes of the numeric type.
Third quartile of standard deviation of attributes of the numeric type.
0.7
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
0.21
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Mean of means among attributes of the numeric type.
0.04
Average mutual information between the nominal attributes and the target attribute.
0.43
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of means among attributes of the numeric type.
0.61
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.23
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
0.21
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.41
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001

7 tasks

546 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: OVERALL_DIAGNOSIS
372 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: OVERALL_DIAGNOSIS
212 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: OVERALL_DIAGNOSIS
166 runs - estimation_procedure: 10-fold Learning Curve - target_feature: OVERALL_DIAGNOSIS
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: OVERALL_DIAGNOSIS
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