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)
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.