Datasets:
| 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. | |