Diabetes Dataset

Context

This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective is to predict based on diagnostic measurements whether a patient has diabetes.

Content

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.

• Pregnancies: Number of times pregnant
• Glucose: Plasma glucose concentration a 2 hours in an oral glucose tolerance test
• BloodPressure: Diastolic blood pressure (mm Hg)
• SkinThickness: Triceps skin fold thickness (mm)
• Insulin: 2-Hour serum insulin (mu U/ml)
• BMI: Body mass index (weight in kg/(height in m)^2)
• DiabetesPedigreeFunction: Diabetes pedigree function
• Age: Age (years)
• Outcome: Class variable (0 or 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

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.

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.

Number of Instances: 768

Number of Attributes: 8 plus class

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)

Missing Attribute Values: Yes

Class Distribution: (class value 1 is interpreted as "tested positive for

diabetes")