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.