Context

Title: Pima Indians Diabetes Database

Content

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

Acknowledgements

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.

Visit

Author: Vincent Sigillito

Source: Obtained from UCI

Please cite: UCI citation policy

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)