To All StatLog partners,

   !!!!!!!!!   PLEASE ACKNOWLEDGE RECEIPT OF THIS FILE  !!!!!!!!!

      The next dataset to be tested is the Pima Indians Diabetes dataset.

Work with this dataset and the German credit dataset should be
completed by Nov 6.

READ THE FOLLOWING VERY CAREFULLY - SOME THINGS ARE NEW.




THIS DATASET SHOULD BE TACKLED VIA 12-FOLD CROSS-VALIDATION.

You should report the following results to Strathclyde - as usual.

      (i)   the confusion matrix for the training set
      (ii)  the confusion matrix for the test set
      (iii) the time for training
      (iv)  the time for testing
      (v)   the storage for training
      (vi)  the storage for testing

(vii)    For partners using machine learning algorithms, we   *******NB******
would also like a copy of the decision trees/rules produced.  
We would also like statistics on the average number of nodes  *******NB******
and size of the trees produced.

(viii)   Please quote the percentage INCORRECTLY classified   *******NB******
in both training and test sets.

*************************************************************

Description of Dataset


1. Title: Pima Indians Diabetes Database

2. Sources:
   (a) Original owners: National Institute of Diabetes and Digestive and
                        Kidney Diseases
   (b) Donor of database: Vincent Sigillito (vgs@aplcen.apl.jhu.edu)
                          Research Center, RMI Group Leader
                          Applied Physics Laboratory
                          The Johns Hopkins University
                          Johns Hopkins Road
                          Laurel, MD 20707
                          (301) 953-6231
   (c) Date received: 9 May 1990

3. Past Usage:
    1. Smith,~J.~W., Everhart,~J.~E., Dickson,~W.~C., Knowler,~W.~C., \&
       Johannes,~R.~S. (1988). Using the ADAP learning algorithm to forecast
       the onset of diabetes mellitus.  In {\it Proceedings of the Symposium
       on Computer Applications and Medical Care} (pp. 261--265).  IEEE
       Computer Society Press.

       The diagnostic, binary-valued variable investigated is whether the
       patient shows signs of diabetes according to World Health Organization
       criteria (i.e., if the 2 hour post-load plasma glucose was at least 
       200 mg/dl at any survey  examination or if found during routine medical
       care).   The population lives near Phoenix, Arizona, USA.

       Results: Their ADAP algorithm makes a real-valued prediction between
       0 and 1.  This was transformed into a binary decision using a cutoff of 
       0.448.  Using 576 training instances, the sensitivity and specificity
       of their algorithm was 76% on the remaining 192 instances.

4. Relevant Information:
      Several constraints were placed on the selection of these instances from
      a larger database.  In particular, all patients here are females at
      least 21 years old of Pima Indian heritage.  ADAP is an adaptive learning
      routine that generates and executes digital analogs of perceptron-like
      devices.  It is a unique algorithm; see the paper for details.

5. Number of Instances: 768

6. Number of Attributes: 8 plus class 

7. For Each Attribute: (all numeric-valued)
   1. Number of times pregnant
   2. Plasma glucose concentration a 2 hours in an oral glucose tolerance test
   3. Diastolic blood pressure (mm Hg)
   4. Triceps skin fold thickness (mm)
   5. 2-Hour serum insulin (mu U/ml)
   6. Body mass index (weight in kg/(height in m)^2)
   7. Diabetes pedigree function
   8. Age (years)
   9. Class variable (1 or 2)

8. Missing Attribute Values: None

9. Class Distribution: (class value 1 is interpreted as "tested positive for
   diabetes")

   Class Value  Number of instances
   1            500
   2            268

10. Brief statistical analysis:

    Attribute number:    Mean:   Standard Deviation:
    1.                     3.8     3.4
    2.                   120.9    32.0
    3.                    69.1    19.4
    4.                    20.5    16.0
    5.                    79.8   115.2
    6.                    32.0     7.9
    7.                     0.5     0.3
    8.                    33.2    11.8


11.   Please also give the confusion matrix with integer entries 
(and not as ratios).

12. Cross Validation

THIS DATASET SHOULD BE TACKLED VIA 12-FOLD CROSS-VALIDATION.
          
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
IF THERE IS ANYTHING HERE YOU DO NOT UNDERSTAND PLEASE CONTACT ME
                    !!! IMMEDIATELY.  !!!!

Ross King
Strathclyde