Information for pairs0041:

Pima Indians Diabetes Database

1. 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

2. 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.

3. 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.  

4. Number of Instances: 768

x: age

y: Plasma glucose concentration a 2 hours in an oral glucose tolerance test


ground truth:
x --> y


UPDATE v1.0: We only kept the instances with nonzero value for plasma glucose concentration,
because we believe these encode for missing values. From the UCI ML webpage:
"UPDATE: Until 02/28/2011 the UCI ML web page indicated that there were no
missing values in the dataset. As pointed out by a repository user, this cannot
be true: there are zeros in places where they are biologically impossible, such
as the blood pressure attribute. It seems very likely that zero values encode
missing data. However, since the dataset donors made no such statement we
encourage you to use your best judgement and state your assumptions."
