Donated on 9/30/2001

Data on cardiac Single Proton Emission Computed Tomography (SPECT) images. Each patient classified into two categories: normal and abnormal.

Dataset Characteristics


Subject Area

Life Science

Associated Tasks


Feature Type


# Instances


# Features


Dataset Information

Additional 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 cardilogists' diagnoses). SPECT is a good data set for testing ML algorithms; it has 267 instances that are descibed by 23 binary attributes

Has Missing Values?


Variables Table

Variable NameRoleTypeDemographicDescriptionUnitsMissing Values

0 to 10 of 22

Additional Variable 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) - dataset is divided into: -- training data ("SPECT.train" 80 instances) -- testing data ("SPECT.test" 187 instances)

Papers Citing this Dataset

EFS: an ensemble feature selection tool implemented as R-package and web-application

By Ursula Neumann, Nikita Genze, Dominik Heider. 2017

Published in BioData mining.

Feature importance scores and lossless feature pruning using Banzhaf power indices

By Bogdan Kulynych, Carmela Troncoso. 2017

Published in ArXiv.

Compensation of feature selection biases accompanied with improved predictive performance for binary classification by using a novel ensemble feature selection approach

By Ursula Neumann, Mona Riemenschneider, Jan-Peter Sowa, Theodor Baars, Julia Kälsch, Ali Canbay, Dominik Heider. 2016

Published in BioData mining.

Interactive Error Correction in Implicative Theories

By Sergei Kuznetsov, Artem Revenko. 2014

Published in ArXiv.

Supervised Classification Using Homogeneous Logical Proportions for Binary and Nominal Features

By Ronei Moraes, Liliane Machado, Henri Prade, Gilles Richard. 2013

Published in CIARP.

0 to 5 of 7

7 citations


Krzysztof Cios

Lukasz Kurgan

Lucy Goodenday


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