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90 lines
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Plaintext
Executable File
90 lines
3.6 KiB
Plaintext
Executable File
1. Title of Database: SPECT 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 84.0% accuracy
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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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SPECT is a good data set for testing ML algorithms; it has 267 instances
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that are descibed by 23 binary attributes
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Other results (in press):
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-- CLIP4 algorithm achieved 86.1% accuracy
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-- ensemble of CLIP4 classifiers achieved 90.4% accuracy
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-- Predicted attribute: OVERALL_DIAGNOSIS (binary)
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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 pattern was further processed to obtain 22 binary feature patterns.
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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 84.0% accurate (as compared with cardilogists' diagnoses).
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5. Number of Instances: 267
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6. Number of Attributes: 23 (22 binary + 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. F1: 0,1 (the partial diagnosis 1, binary)
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3. F2: 0,1 (the partial diagnosis 2, binary)
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4. F3: 0,1 (the partial diagnosis 3, binary)
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5. F4: 0,1 (the partial diagnosis 4, binary)
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6. F5: 0,1 (the partial diagnosis 5, binary)
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7. F6: 0,1 (the partial diagnosis 6, binary)
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8. F7: 0,1 (the partial diagnosis 7, binary)
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9. F8: 0,1 (the partial diagnosis 8, binary)
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10. F9: 0,1 (the partial diagnosis 9, binary)
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11. F10: 0,1 (the partial diagnosis 10, binary)
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12. F11: 0,1 (the partial diagnosis 11, binary)
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13. F12: 0,1 (the partial diagnosis 12, binary)
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14. F13: 0,1 (the partial diagnosis 13, binary)
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15. F14: 0,1 (the partial diagnosis 14, binary)
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16. F15: 0,1 (the partial diagnosis 15, binary)
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17. F16: 0,1 (the partial diagnosis 16, binary)
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18. F17: 0,1 (the partial diagnosis 17, binary)
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19. F18: 0,1 (the partial diagnosis 18, binary)
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20. F19: 0,1 (the partial diagnosis 19, binary)
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21. F20: 0,1 (the partial diagnosis 20, binary)
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22. F21: 0,1 (the partial diagnosis 21, binary)
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23. F22: 0,1 (the partial diagnosis 22, binary)
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-- dataset is divided into:
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-- training data ("SPECT.train" 80 instances)
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-- testing data ("SPECT.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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