Commit Inicial

data/tanveer/spect/SPECT.names

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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