pima-indians-diabetes.names

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 a
   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)nd 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)


8. Missing Attribute Values: Yes

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