"1. Title of Database: SPECTF 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 77.0% accuracy. CLIP3 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 SPECTF is a good data set for testing ML algorithms; it has 267 instances that are descibed by 45 attributes. Predicted attribute: OVERALL_DIAGNOSIS (binary) NOTE: See the SPECT heart data for binary data for the same classification task. 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 CLIP3 algorithm was used to generate classification rules from these patterns. The CLIP3 algorithm generated rules that were 77.0% accurate (as compared with cardilogists' diagnoses). 5. Number of Instances: 267 6. Number of Attributes: 45 (44 continuous + 1 binary class) 7. Attribute Information: 1. OVERALL_DIAGNOSIS: 0,1 (class attribute, binary) 2. F1R: continuous (count in ROI (region of interest) 1 in rest) 3. F1S: continuous (count in ROI 1 in stress) 4. F2R: continuous (count in ROI 2 in rest) 5. F2S: continuous (count in ROI 2 in stress) 6. F3R: continuous (count in ROI 3 in rest) 7. F3S: continuous (count in ROI 3 in stress) 8. F4R: continuous (count in ROI 4 in rest) 9. F4S: continuous (count in ROI 4 in stress) 10. F5R: continuous (count in ROI 5 in rest) 11. F5S: continuous (count in ROI 5 in stress) 12. F6R: continuous (count in ROI 6 in rest) 13. F6S: continuous (count in ROI 6 in stress) 14. F7R: continuous (count in ROI 7 in rest) 15. F7S: continuous (count in ROI 7 in stress) 16. F8R: continuous (count in ROI 8 in rest) 17. F8S: continuous (count in ROI 8 in stress) 18. F9R: continuous (count in ROI 9 in rest) 19. F9S: continuous (count in ROI 9 in stress) 20. F10R: continuous (count in ROI 10 in rest) 21. F10S: continuous (count in ROI 10 in stress) 22. F11R: continuous (count in ROI 11 in rest) 23. F11S: continuous (count in ROI 11 in stress) 24. F12R: continuous (count in ROI 12 in rest) 25. F12S: continuous (count in ROI 12 in stress) 26. F13R: continuous (count in ROI 13 in rest) 27. F13S: continuous (count in ROI 13 in stress) 28. F14R: continuous (count in ROI 14 in rest) 29. F14S: continuous (count in ROI 14 in stress) 30. F15R: continuous (count in ROI 15 in rest) 31. F15S: continuous (count in ROI 15 in stress) 32. F16R: continuous (count in ROI 16 in rest) 33. F16S: continuous (count in ROI 16 in stress) 34. F17R: continuous (count in ROI 17 in rest) 35. F17S: continuous (count in ROI 17 in stress) 36. F18R: continuous (count in ROI 18 in rest) 37. F18S: continuous (count in ROI 18 in stress) 38. F19R: continuous (count in ROI 19 in rest) 39. F19S: continuous (count in ROI 19 in stress) 40. F20R: continuous (count in ROI 20 in rest) 41. F20S: continuous (count in ROI 20 in stress) 42. F21R: continuous (count in ROI 21 in rest) 43. F21S: continuous (count in ROI 21 in stress) 44. F22R: continuous (count in ROI 22 in rest) 45. F22S: continuous (count in ROI 22 in stress) -- all continuous attributes have integer values from the 0 to 100 -- dataset is divided into: -- training data (\"SPECTF.train\" 80 instances) -- testing data (\"SPECTF.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 NOTE: See the SPECT heart data for binary data for the same classification task. Information about the dataset CLASSTYPE: nominal CLASSINDEX: first " "0" "spect" 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 59 72 71 69 70 57 69 61 65 74 70 67 76 70 78 67 70 63 62 80 63 70 61 70 75 77 75 78 69 72 64 72 79 66 65 71 70 73 68 68 62 62 59 75 77 68 76 61 67 71 64 76 74 65 59 76 71 80 68 67 72 62 69 59 66 62 71 63 74 69 70 65 64 70 72 70 59 67 68 66 52 62 62 71 66 69 66 60 62 73 69 66 69 69 73 51 54 63 56 74 58 65 63 64 71 61 72 76 68 66 64 63 78 81 66 75 66 76 76 64 67 67 68 75 79 64 73 76 65 61 70 75 73 67 75 72 75 76 70 57 74 71 78 65 67 66 71 61 81 64 64 68 53 71 70 75 57 64 65 54 70 69 70 70 61 68 62 60 67 72 60 65 78 67 68 73 66 69 66 82 66 65 58 52 54 62 75 71 75 75 70 68 66 75 71 76 66 68 79 65 64 68 69 70 79 74 74 71 77 74 71 68 72 69 70 73 78 75 66 73 67 78 74 53 63 66 69 75 80 73 68 70 74 71 75 72 67 73 72 69 67 67 64 78 66 75 75 62 68 79 62 77 73 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