spect_test.arff

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UCI/spect_test.arff
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2009-10-30 13:16:09
Description

SPECT heart data
File contents

% 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}

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1,1,0,0,0,1,1,0,0,1,1,1,0,0,1,0,0,0,0,0,0,0,0
1,1,1,1,0,0,0,0,0,0,0,0,1,1,0,0,1,1,1,0,0,1,1
1,1,0,1,0,1,0,0,1,0,1,0,0,1,0,0,0,1,0,1,1,1,0
1,1,1,0,1,1,0,0,1,1,1,0,1,0,0,1,1,0,0,0,1,1,0
1,0,0,1,1,0,0,0,1,0,0,0,0,1,1,0,1,0,0,0,1,1,1
1,0,0,1,0,0,0,0,1,0,0,0,1,1,0,0,0,0,0,0,0,1,0
1,0,1,1,1,0,1,1,1,1,0,1,0,1,1,1,1,0,0,0,1,1,1
1,0,1,1,0,0,1,0,1,0,0,1,1,1,0,0,1,0,0,1,1,0,1
1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,0,0,1,1,1,1
1,1,0,1,0,1,0,1,0,1,1,0,0,1,1,1,1,0,0,1,1,0,1
1,1,0,0,1,1,0,0,0,1,1,0,0,0,1,1,1,0,0,1,1,0,1
1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1
1,1,1,1,0,0,1,1,0,1,0,0,1,0,1,0,1,0,0,0,1,1,1
1,0,0,1,0,0,0,0,1,0,0,1,0,1,0,0,1,0,0,0,1,1,1
1,1,1,1,1,1,1,1,0,1,1,0,0,1,0,1,1,0,0,1,1,1,1
1,1,0,0,0,1,0,0,1,1,1,0,0,1,1,0,1,0,0,1,1,0,0
1,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,1
1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0
1,1,1,1,0,1,0,1,1,0,1,0,1,1,0,0,0,0,0,0,0,1,0
1,1,1,1,0,0,1,1,1,0,1,1,1,1,0,0,0,1,1,1,1,0,0
1,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,1,0,1,1
1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1
1,0,0,0,1,0,0,0,1,1,0,0,0,1,1,0,0,0,0,0,0,1,0
1,1,1,0,0,1,1,0,0,1,1,1,0,0,0,0,0,0,0,1,0,0,0
1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1
1,1,1,1,1,1,1,1,1,0,1,1,1,1,0,1,1,1,1,1,1,1,1
1,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0,1,0,0
1,1,0,1,1,1,1,1,1,0,0,1,1,1,1,1,0,1,0,0,0,1,1
1,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,1,0
1,0,0,1,1,0,0,0,1,1,1,0,1,1,1,1,0,0,0,0,0,1,1
1,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0,0,0,0
1,0,0,1,0,1,0,0,1,0,1,0,0,1,0,1,0,0,1,0,0,1,0
1,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,1,1,0,0,1,1
1,0,1,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1
1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
1,1,1,1,0,1,1,0,0,0,1,1,0,0,0,0,1,0,0,0,1,0,0
1,1,0,1,1,1,0,0,1,1,1,0,0,1,1,0,1,0,1,1,1,0,0
1,1,0,0,0,1,0,0,0,1,1,0,0,0,1,0,0,0,0,0,1,1,0
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
0,0,0,1,1,0,0,0,1,1,0,0,0,1,1,0,0,0,0,0,0,1,0
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,1
0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0
0,0,0,1,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
0,1,1,0,0,0,1,0,0,0,1,1,1,0,0,0,1,0,0,0,0,0,0
0,1,0,1,0,1,0,0,1,0,0,0,0,1,0,1,1,0,0,0,0,0,0
0,1,0,1,0,1,0,0,1,1,0,0,0,0,1,0,1,0,0,0,0,0,0
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0

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