‰HDF  ˙˙˙˙˙˙˙˙Ču˙˙˙˙˙˙˙˙`ˆ¨ TREE˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙čHEAPX Čdatadata_descr8ˆ¨ (namespect (mldata0 ˆcommenta1. 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. 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Če[ČeZČeYČeVČeWČeXČePČeQČeRČeSČeTČeUČeEČeFČeGČeHČeIČeJČeKČeLČeMČeNČeOČe/Če0Če1Če2Če3Če4Če5Če6Če7Če8Če9Če:Če;Če<Če=Če>Če?Če@ČeAČeBČeCČeDGCOLF12RF12SF13RF13SF14RF14SF15RF15S F16R F16S F17R F17S F18RF18SF19RF19SF20RF20SF21RF21SF22RF22SF6SF7RF7SF8RF8SF9RF9SF10RF10S F11R!F11S"F3S#F4R$F4S%F5R&F5S'F6R(F2R)F2S*F3R+F1S,F1R-OVERALL_DIAGNOSIS.int0/numeric0numeric1numeric2numeric3numeric4numeric5numeric6numeric7numeric8numeric9numeric:numeric;numeric<numeric=numeric>numeric?numeric@numericAnumericBnumericCnumericDnumericEnumericFnumericGnumericHnumericInumericJnumericKnumericLnumericMnumericNnumericOnumericPnumericQnumericRnumericSnumericTnumericUnumericVnumericWnumericXnumericYnumericZnumeric[ nominal:0,1P