• Original owners: National Institute of Diabetes and Digestive and Kidney Diseases
• Donor of database: Vincent Sigillito ([email protected]) 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

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 "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.

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.

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)

(class value 1 is interpreted as "tested positive for diabetes")

Class Value    Number of instances
0               500
1               268

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