Info on Credit Approval Data Base

1. Title: Echocardiogram Data

2. Source Information:
   -- Donor: Steven Salzberg (salzberg@cs.jhu.edu)
   -- Collector:
      -- Dr. Evlin Kinney
      -- The Reed Institute
      -- P.O. Box 402603
      -- Maimi, FL 33140-0603
   -- Date Received: 28 February 1989

3. Past Usage:
   -- 1. Salzberg, S. (1988).  Exemplar-based learning: Theory and
         implementation (Technical Report TR-10-88).  Harvard University,
         Center for Research in Computing Technology, Aiken Computation
         Laboratory (33 Oxford Street; Cambridge, MA 02138).
      -- Steve applied his EACH program to predict survival (i.e., life
         or death), did not use the wall-motion attribute, and recorded 87 
         correct and 29 incorrect in an incremental application to this
         database.  He also showed that, by tuning EACH to this domain,
         EACH was able to derive (non-incrementally) a set of 28 
         hyper-rectangles that could perfectly classify 119 instances.
   -- 2. Kan, G., Visser, C., Kooler, J., & Dunning, A. (1986).  Short
         and long term predictive value of wall motion score in acute 
         myocardial infarction.  British Heart Journal, 56, 422-427.
      -- They predicted the same variable (whether patients will live
         one year after a heart attack) using a different set of 345
         instances.  Their statistical test recorded a 61% accuracy
         in predicting that a patient will die (post-hoc fit).
   -- 3. Elvin Kinney (in communication with Steven Salzberg) reported
         that a Cox regression application recorded a 60% accuracy
         in predicting that a patient will die.

4. Relevant Information:
  -- All the patients suffered heart attacks at some point in the past.
     Some are still alive and some are not.  The survival and still-alive
     variables, when taken together, indicate whether a patient survived
     for at least one year following the heart attack.  

     The problem addressed by past researchers was to predict from the 
     other variables whether or not the patient will survive at least
     one year.  The most difficult part of this problem is correctly
     predicting that the patient will NOT survive.  (Part of the difficulty
     seems to be the size of the data set.)

5. Number of Instances: 132

6. Number of Attributes: 13 (all numeric-valued)

7. Attribute Information:
   1. survival -- the number of months patient survived (has survived,
		  if patient is still alive).  Because all the patients
		  had their heart attacks at different times, it is 
		  possible that some patients have survived less than
		  one year but they are still alive.  Check the second
		  variable to confirm this.  Such patients cannot be 
		  used for the prediction task mentioned above.
   2. still-alive -- a binary variable.  0=dead at end of survival period,
		     1 means still alive 
   3. age-at-heart-attack -- age in years when heart attack occurred
   4. pericardial-effusion -- binary. Pericardial effusion is fluid
			      around the heart.  0=no fluid, 1=fluid
   5. fractional-shortening -- a measure of contracility around the heart
			       lower numbers are increasingly abnormal
   6. epss -- E-point septal separation, another measure of contractility.  
	      Larger numbers are increasingly abnormal.
   7. lvdd -- left ventricular end-diastolic dimension.  This is
	      a measure of the size of the heart at end-diastole.
	      Large hearts tend to be sick hearts.
   8. wall-motion-score -- a measure of how the segments of the left
			   ventricle are moving
   9. wall-motion-index -- equals wall-motion-score divided by number of
			   segments seen.  Usually 12-13 segments are seen
			   in an echocardiogram.  Use this variable INSTEAD
			   of the wall motion score.
   10. mult -- a derivate var which can be ignored
   11. name -- the name of the patient (I have replaced them with "name")
   12. group -- meaningless, ignore it
   13. alive-at-1 -- Boolean-valued. Derived from the first two attributes.
                     0 means patient was either dead after 1 year or had
                     been followed for less than 1 year.  1 means patient 
                     was alive at 1 year.

8. Missing Attribute Values: (denoted by "?")
   Attribute #:    Number of Missing Values: (total: 132)
   ------------    -------------------------
              1    2  
              2	   1  
              3	   5  
              4	   1  
              5	   8  
              6	   15 
              7	   11 
              8	   4  
              9	   1  
             10	   4 
             11	   0 
             12	   22
             13	   58

9. Distribution of attribute number 2: still-alive
   Value   Number of instances with this value
    ----   -----------------------------------
      0    88 (dead)
      1    43 (alive)
      ?    1
    Total  132


10. Distribution of attribute number 13: alive-at-1
   Value   Number of instances with this value
    ----   -----------------------------------
      0    50
      1    24
      ?    58
    Total  132