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UCI/spect_test.arff
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2009-10-30 13:16:09
Description
SPECT heart data
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% 1. Title of Database: SPECT heart data % % 2. 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 % % 3. Past Usage: % 1. Kurgan, L.A., Cios, K.J., Tadeusiewicz, R., Ogiela, M. & Goodenday, L.S. % "Knowledge Discovery Approach to Automated Cardiac SPECT Diagnosis" % Artificial Intelligence in Medicine, vol. 23:2, pp 149-169, Oct 2001 % % Results: The CLIP3 machine learning algorithm achieved 84.0% accuracy % References: % Cios, K.J., Wedding, D.K. & Liu, N. % CLIP3: cover learning using integer programming. % Kybernetes, 26:4-5, pp 513-536, 1997 % % Cios, K.J. & Kurgan, L. % Hybrid Inductive Machine Learning: An Overview of CLIP Algorithms, % In: Jain, L.C., and Kacprzyk, J. (Eds.) % New Learning Paradigms in Soft Computing, % Physica-Verlag (Springer), 2001 % % SPECT is a good data set for testing ML algorithms; it has 267 instances % that are descibed by 23 binary attributes % % Other results (in press): % -- CLIP4 algorithm achieved 86.1% accuracy % -- ensemble of CLIP4 classifiers achieved 90.4% accuracy % -- Predicted attribute: OVERALL_DIAGNOSIS (binary) % % 4. 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 cardilogists' diagnoses). % % 5. Number of Instances: 267 % 6. Number of Attributes: 23 (22 binary + 1 binary class) % 7. 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) % -- dataset is divided into: % -- training data ("SPECT.train" 80 instances) % -- testing data ("SPECT.test" 187 instances) % 8. Missing Attribute Values: None % 9. Class Distribution: % -- entire data % Class # examples % 0 55 % 1 212 % -- training dataset % Class # examples % 0 40 % 1 40 % -- testing dataset % Class # examples % 0 15 % 1 172 % % Information about the dataset % CLASSTYPE: nominal % CLASSINDEX: first % @relation spect @attribute OVERALL_DIAGNOSIS {0,1} @attribute F1 {0,1} @attribute F2 {0,1} @attribute F3 {0,1} @attribute F4 {0,1} @attribute F5 {0,1} @attribute F6 {0,1} @attribute F7 {0,1} @attribute F8 {0,1} @attribute F9 {0,1} @attribute F10 {0,1} @attribute F11 {0,1} @attribute F12 {0,1} @attribute F13 {0,1} @attribute F14 {0,1} @attribute F15 {0,1} @attribute F16 {0,1} @attribute F17 {0,1} @attribute F18 {0,1} @attribute F19 {0,1} @attribute F20 {0,1} @attribute F21 {0,1} @attribute F22 {0,1} @data 1,1,0,0,1,1,0,0,0,1,1,0,0,0,1,1,1,0,0,1,1,0,0 1,1,0,0,1,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0 1,0,0,0,1,0,1,0,0,1,0,1,0,0,1,1,0,0,0,0,0,0,1 1,0,1,1,1,0,0,1,0,1,0,0,1,1,1,0,1,0,0,0,0,1,0 1,0,0,1,0,0,0,0,1,0,0,1,0,1,1,0,1,0,0,0,0,0,1 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