*********** Diabetes.nns is a NeuNet Pro Sample File ***********

This file contains special properties that allow NeuNet Pro to recognize it
as authorized NeuNet sample data.  Anyone using the unlicensed version
of NeuNet Pro is welcome to experiment with this sample data.
Please do not modify this file, or it will lose its status as authorized sample data.

For further information about Neunet Pro and additional sample data,
please visit the NeuNet Pro website at http://www.cormactech.com/neunet

All of this data has been collected from publicly available sources.
CorMac Technologies Inc. does not guarantee the accuracy of the data.
This data is intended solely for experimental purposes.


    ***************  More about the Diabetes Data *******************


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 (0 or 1)

8. Missing Attribute Values: None

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

   Class Value  Number of instances
   0            500
   1            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