Data
SPECTF

SPECTF

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Author: Krzysztof J. Cios","Lukasz A. Source: [original](https://archive.ics.uci.edu/ml/datasets/SPECTF+Heart) - Date unknown Please cite: This is a corrected version of the previous data file in version 1, which contained a dataset (349 instances) incorrectly merged from the original training and test sets available on UCI (there are actually 2 test sets there, one of them is incorrect). This file fixes that problem by merging the training set with the correct test set, resulting 267 instances. SPECTF 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. NOTE: See the SPECT heart data for binary data for the same classification task. 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 CLIP3 algorithm was used to generate classification rules from these patterns. The CLIP3 algorithm generated rules that were 77.0% accurate (as compared with cardiologists' diagnoses).

45 features

OVERALL_DIAGNOSIS (target)nominal2 unique values
0 missing
F1Rnumeric39 unique values
0 missing
F1Snumeric43 unique values
0 missing
F2Rnumeric34 unique values
0 missing
F2Snumeric40 unique values
0 missing
F3Rnumeric44 unique values
0 missing
F3Snumeric44 unique values
0 missing
F4Rnumeric37 unique values
0 missing
F4Snumeric42 unique values
0 missing
F5Rnumeric37 unique values
0 missing
F5Snumeric44 unique values
0 missing
F6Rnumeric33 unique values
0 missing
F6Snumeric37 unique values
0 missing
F7Rnumeric39 unique values
0 missing
F7Snumeric36 unique values
0 missing
F8Rnumeric46 unique values
0 missing
F8Snumeric47 unique values
0 missing
F9Rnumeric37 unique values
0 missing
F9Snumeric42 unique values
0 missing
F10Rnumeric41 unique values
0 missing
F10Snumeric39 unique values
0 missing
F11Rnumeric32 unique values
0 missing
F11Snumeric37 unique values
0 missing
F12Rnumeric40 unique values
0 missing
F12Snumeric43 unique values
0 missing
F13Rnumeric56 unique values
0 missing
F13Snumeric60 unique values
0 missing
F14Rnumeric42 unique values
0 missing
F14Snumeric51 unique values
0 missing
F15Rnumeric49 unique values
0 missing
F15Snumeric51 unique values
0 missing
F16Rnumeric29 unique values
0 missing
F16Snumeric36 unique values
0 missing
F17Rnumeric36 unique values
0 missing
F17Snumeric38 unique values
0 missing
F18Rnumeric36 unique values
0 missing
F18Snumeric44 unique values
0 missing
F19Rnumeric37 unique values
0 missing
F19Snumeric40 unique values
0 missing
F20Rnumeric51 unique values
0 missing
F20Snumeric50 unique values
0 missing
F21Rnumeric56 unique values
0 missing
F21Snumeric61 unique values
0 missing
F22Rnumeric52 unique values
0 missing
F22Snumeric59 unique values
0 missing

62 properties

267
Number of instances (rows) of the dataset.
45
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.
44
Number of numeric attributes.
1
Number of nominal attributes.
2
The maximum number of distinct values among attributes of the nominal type.
-3.12
Minimum skewness among attributes of the numeric type.
2.22
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
-0.88
Maximum skewness among attributes of the numeric type.
6.06
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
-1.24
Third quartile of skewness among attributes of the numeric type.
15.2
Maximum standard deviation of attributes of the numeric type.
20.6
Percentage of instances belonging to the least frequent class.
2.48
First quartile of kurtosis among attributes of the numeric type.
11.24
Third quartile of standard deviation of attributes of the numeric type.
Average entropy of the attributes.
55
Number of instances belonging to the least frequent class.
61.58
First quartile of means among attributes of the numeric type.
0
Standard deviation of the number of distinct values among attributes of the nominal type.
5.22
Mean kurtosis among attributes of the numeric type.
1
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
64.58
Mean of means among attributes of the numeric type.
-2.27
First quartile of skewness among attributes of the numeric type.
0.99
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
7.98
First quartile of standard deviation of attributes of the numeric type.
0.73
Entropy of the target attribute values.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
Second quartile (Median) of entropy among attributes.
0.17
Number of attributes divided by the number of instances.
2
Average number of distinct values among the attributes of the nominal type.
3.95
Second quartile (Median) of kurtosis among attributes of the numeric type.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
-1.76
Mean skewness among attributes of the numeric type.
64.81
Second quartile (Median) of means among attributes of the numeric type.
79.4
Percentage of instances belonging to the most frequent class.
9.68
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
212
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
-1.66
Second quartile (Median) of skewness among attributes of the numeric type.
Maximum entropy among attributes.
0.84
Minimum kurtosis among attributes of the numeric type.
2.22
Percentage of binary attributes.
9.19
Second quartile (Median) of standard deviation of attributes of the numeric type.
16.91
Maximum kurtosis among attributes of the numeric type.
50.54
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
74.03
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.
7.09
Third quartile of kurtosis among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
97.78
Percentage of numeric attributes.
67.44
Third quartile of means among attributes of the numeric type.

4 tasks

0 runs - estimation_procedure: 33% Holdout set - target_feature: OVERALL_DIAGNOSIS
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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