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data/tanveer/spectf/SPECTF.names
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data/tanveer/spectf/SPECTF.names
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1. Title of Database: SPECTF heart data
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2. Sources:
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-- Original owners: Krzysztof J. Cios, Lukasz A. Kurgan
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University of Colorado at Denver, Denver, CO 80217, U.S.A.
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Krys.Cios@cudenver.edu
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Lucy S. Goodenday
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Medical College of Ohio, OH, U.S.A.
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-- Donors: Lukasz A.Kurgan, Krzysztof J. Cios
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-- Date: 10/01/01
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3. Past Usage:
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1. Kurgan, L.A., Cios, K.J., Tadeusiewicz, R., Ogiela, M. & Goodenday, L.S.
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"Knowledge Discovery Approach to Automated Cardiac SPECT Diagnosis"
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Artificial Intelligence in Medicine, vol. 23:2, pp 149-169, Oct 2001
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Results: The CLIP3 machine learning algorithm achieved 77.0% accuracy.
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CLIP3 references:
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Cios, K.J., Wedding, D.K. & Liu, N.
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CLIP3: cover learning using integer programming.
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Kybernetes, 26:4-5, pp 513-536, 1997
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Cios K. J. & Kurgan L.
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Hybrid Inductive Machine Learning: An Overview of CLIP Algorithms,
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In: Jain L.C., and Kacprzyk J. (Eds.)
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New Learning Paradigms in Soft Computing,
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Physica-Verlag (Springer), 2001
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SPECTF is a good data set for testing ML algorithms; it has 267 instances that are descibed by 45 attributes.
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Predicted attribute: OVERALL_DIAGNOSIS (binary)
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NOTE: See the SPECT heart data for binary data for the same classification task.
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4. Relevant Information:
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The dataset describes diagnosing of cardiac Single Proton Emission Computed Tomography (SPECT) images.
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Each of the patients is classified into two categories: normal and abnormal.
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The database of 267 SPECT image sets (patients) was processed to extract features that summarize the original SPECT images.
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As a result, 44 continuous feature pattern was created for each patient.
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The CLIP3 algorithm was used to generate classification rules from these patterns.
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The CLIP3 algorithm generated rules that were 77.0% accurate (as compared with cardilogists' diagnoses).
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5. Number of Instances: 267
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6. Number of Attributes: 45 (44 continuous + 1 binary class)
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7. Attribute Information:
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1. OVERALL_DIAGNOSIS: 0,1 (class attribute, binary)
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2. F1R: continuous (count in ROI (region of interest) 1 in rest)
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3. F1S: continuous (count in ROI 1 in stress)
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4. F2R: continuous (count in ROI 2 in rest)
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5. F2S: continuous (count in ROI 2 in stress)
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6. F3R: continuous (count in ROI 3 in rest)
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7. F3S: continuous (count in ROI 3 in stress)
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8. F4R: continuous (count in ROI 4 in rest)
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9. F4S: continuous (count in ROI 4 in stress)
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10. F5R: continuous (count in ROI 5 in rest)
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11. F5S: continuous (count in ROI 5 in stress)
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12. F6R: continuous (count in ROI 6 in rest)
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13. F6S: continuous (count in ROI 6 in stress)
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14. F7R: continuous (count in ROI 7 in rest)
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15. F7S: continuous (count in ROI 7 in stress)
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16. F8R: continuous (count in ROI 8 in rest)
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17. F8S: continuous (count in ROI 8 in stress)
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18. F9R: continuous (count in ROI 9 in rest)
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19. F9S: continuous (count in ROI 9 in stress)
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20. F10R: continuous (count in ROI 10 in rest)
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21. F10S: continuous (count in ROI 10 in stress)
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22. F11R: continuous (count in ROI 11 in rest)
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23. F11S: continuous (count in ROI 11 in stress)
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24. F12R: continuous (count in ROI 12 in rest)
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25. F12S: continuous (count in ROI 12 in stress)
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26. F13R: continuous (count in ROI 13 in rest)
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27. F13S: continuous (count in ROI 13 in stress)
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28. F14R: continuous (count in ROI 14 in rest)
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29. F14S: continuous (count in ROI 14 in stress)
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30. F15R: continuous (count in ROI 15 in rest)
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31. F15S: continuous (count in ROI 15 in stress)
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32. F16R: continuous (count in ROI 16 in rest)
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33. F16S: continuous (count in ROI 16 in stress)
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34. F17R: continuous (count in ROI 17 in rest)
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35. F17S: continuous (count in ROI 17 in stress)
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36. F18R: continuous (count in ROI 18 in rest)
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37. F18S: continuous (count in ROI 18 in stress)
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38. F19R: continuous (count in ROI 19 in rest)
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39. F19S: continuous (count in ROI 19 in stress)
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40. F20R: continuous (count in ROI 20 in rest)
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41. F20S: continuous (count in ROI 20 in stress)
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42. F21R: continuous (count in ROI 21 in rest)
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43. F21S: continuous (count in ROI 21 in stress)
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44. F22R: continuous (count in ROI 22 in rest)
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45. F22S: continuous (count in ROI 22 in stress)
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-- all continuous attributes have integer values from the 0 to 100
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-- dataset is divided into:
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-- training data ("SPECTF.train" 80 instances)
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-- testing data ("SPECTF.test" 187 instances)
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8. Missing Attribute Values: None
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9. Class Distribution:
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-- entire data
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Class # examples
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0 55
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1 212
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-- training dataset
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Class # examples
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0 40
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1 40
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-- testing dataset
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Class # examples
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0 15
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1 172
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NOTE: See the SPECT heart data for binary data for the same classification task.
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